... Title : THE CLASSIFICATION OF ERRORS INVOLVED IN THE DETERMINATION OF CHANCES OF HIT, AND THE EVALUATION OF CHANCE ...

Markovian analysis is a method to measure optical texture based on gray-level transition probabilities in digitized images. Experiments are described that investigate the classification performance of parameters generated by Markovian analysis. Results using Markov texture parameters show that the selection of a Markov step size strongly affects ...

This paper describes an application of the Minimum Classification Error (MCE) criterion to the problem of recognizing online unconstrained-style characters and words. We describe an HMM-based, character and word-level MCE training aimed at minimizing the character or word error rate while enabling flexibility in writing style through ...

... Subject index to significant articles, news items, and editorials from ... Classification, and Frequency of Medical Errors in Outpatient Diabetes Care. ...

... April 1995 Accuracy Assessment of the Discrete Classification of Remotely- Sensed Digital ... classification's locational accuracy. The use of an error ...

Improving the classification performance is a crucial step of any machine learning method. In order to achieve a better classification Support Vector Machines need to tune parameters and to select relevant variables. To simultaneously perform both targets an embedded approach can be considered. This method consists of a two-layer ...

... ADD752902. Title : THE ESTIMATION OF THE PROBABILITY OF ERROR IN HUMAN/ATR CLASSIFICATION PERFORMANCE. ...

Human error is a primary or contributing factor in about two-thirds of commercial aviation accidents

, squared error loss, boosting loss (which we derive from boosting's exponential loss), and cost-weighted by concentrating () iii #12;near c. We also show that cost-weighting uncalibrated loss functions can achieve tailoring . . . . . . . . . . . . . . . . . 9 2.3 Cost-Weighted Misclassification Error Loss . . . . . . . . . . . . . . . ...

Former research on inverse halftoning most focus on developing a general-purpose method for all types of halftone patterns, such as error diffusion, ordered dithering, etc., while fail to consider the natural discrepancies among various halftoning methods. To achieve optimal image quality for each halftoning method, the classification ...

In this paper, the problem of multimedia object identification in channels with asymmetric desynchronizations is studied. First, we analyze the achievable rates attainable in such protocols within digital communication framework. Secondly, we investigate the impact of the fingerprint length on the error performance of these protocols relaxing the capacity ...

BackgroundProtein domain classification is an important step in metagenomic annotation. The state-of-the-art method for protein domain classification is profile HMM-based alignment. However, the relatively high rates of insertions and deletions in homopolymer regions of pyrosequencing reads create frameshifts, causing conventional profile HMM alignment ...

Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper an ERP-signal classification method is proposed for discriminating between ERPs of correct and incorrect responses of actors and of observers seeing an actor making such ...

... 49 5.21 Classification error for k-nearest-neighbor ..... 49 ... eq Quantization error. 9t Region of d-dimensional space. ...

This paper presents a novel classification scheme for SAR images based on the perceptual classification of image patterns in the Discrete Hermite Transform (DHT) domain over a roughly hexagonal sampling lattice. The DHT analyzes a signal through a set of binomial filters which approximate the Gaussian derivatives with the advantage that they are computed ...

Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis ...

With the implementation of the No Child Left Behind assessment program and the use of proficiency levels as a means of evaluating Annual Yearly Progress, there is a renewed interest in the consistency of classification decisions based on scale scores from achievement test and state-wide proficiency standards. Many of the current methods described in the ...

Text categorization is a fundamental task in document processing, allowing the automated handling of enormous streams of documents in electronic form. One difficulty in handling some classes of documents is the presence of different kinds of textual errors, such as spelling and grammatical errors in email, and character recognition ...

A taxonomy is an ordered classification system. In error reduction analysis, an examination of the human failures that lead to an event often uses a taxonomy to classify the failures according to some aspect of their characteristics. These classifications provide insights into the forces that kept the persons involved from ...

Data from the 1983-84 Israeli IEA (International Association for the Evaluation of Education Achievement) science study were used to explore gender-related differences (and their determinants) in the learning of science. The sample was composed of 1,934 ninth-grade students. The study involved several measures of science learning, ten attitudinal measures, and items and ...

The report contains statistical tables and figures derived from data obtained during a recent inventory of east Texas. The multiresource inventory included 43 counties and two survey regions. Classification of forest-nonforest points was accomplished, each representing approximately 230 acres. The sampling methods are designed to achieve suitable sampling ...

... of Medical Errors in Outpatient Diabetes Care ... Abstract Objectives: Diabetes-related medical errors in outpatient practice are common and costly. ...

We describe an adaptive approach for the classification of multichannel neural recordings for a brain computer interface. A dual-tree undecimated wavelet packet transform generates a structured redundant feature dictionary with different time-frequency resolutions computed on multichannel neural recordings. Rather than evaluating the individual discrimination performance of ...

The classification of 242 measurements in 14 classes is attempted using two di#erent classification approaches. Measurements have been performed with a commercial Electronic Nose comprising 11 chemical sensors on extra-virgin olive oils of 14 different geographical provenances. As we deal with a relatively small data set and a big number of classes, the ...

... Descriptors : *INFORMATION RETRIEVAL, *CATALOGS, *SUBJECT INDEXING ... SHARING, INDEXES, RECORDS, CLASSIFICATION, ERRORS. ...

This paper presents local spline regression for semi-supervised classification. The core idea in our approach is to introduce splines developed in Sobolev space to map the data points directly to be class labels. The spline is composed of polynomials and Green's functions. It is smooth, nonlinear, and able to interpolate the scattered data points with high accuracy. ...

The ability to identify speech acts reliably is desirable in any spoken language system that interacts with humans. Minimally, such a system should be capable of distinguishing between question-bearing turns and other types of utterances. However, this is a non-trivial task, since spontaneous speech tends to have incomplete syntactic, and even ungrammatical, structure and is characterized by ...

With the coming data deluge from synoptic surveys, there is a need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly observed variables based on small numbers of time-series measurements. In this paper, we introduce a methodology for variable-star classification, drawing from modern machine-learning ...

BackgroundWith DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, ...

Many methods for automatic heartbeat classification have been applied and reported in literature, but relatively few of them concerned with patient independent classification because of the less significant results compared to patient dependent ones. In this work, using phase space reconstruction in order to classify five heartbeat types can fill this gap ...

This study focuses on evaluation of a crop classification from middle-resolution images (Envisat MERIS) at national level. The main goal of such Land Use product is to provid spatial data for optimisation of monitoring of surface and groundwater pollution in the Czech Republic caused by pesticides use in agriculture. As there is a lack of spatial data on the pesticide use and ...

Discrete classification is common in Genomic Signal Processing applications, in particular in classification of discretized gene expression data, and in discrete gene expression prediction and the inference of boolean genomic regulatory networks. Once a discrete classifier is obtained from sample data, its performance must be evaluated through its ...

The study evolved over two phases. First, an existing artificial intelligence technique, heuristic state space search, was used to successfully address and resolve significant issues that have prevented automated error classification in the past. A general method was devised for constructing heuristic functions to guide the search process, which ...

The ability of commonly used similarity coefficients to recapture natural classifications in the presence of various types of errors is investigated by means of several computer simulations of a certain model of the classification problem. The goal is to ...

... 35 4.2.1 MUltiple SIgnal Classification (MUSIC) .... 37 ... called MUltiple SIgnal Classification (MUSIC) and Estimation of Signals Parameters ...

... 4 and a fictitious HH shell fired from the 20 pr tank gun in Centurion ... usual) that the total errors in line and elevation are sufficiently Gaussian in form ...

Jun 16, 2011 ... Classification errors for community models averaged 19%, but the errors were reduced by half when adjusted for misalignment between ...

... AN ANALYSIS OF STOPPING CRITERIA ... 4.5.2 The Total Absolute Error Conclusions. ... decreased, the classification error rate and the variance of ...

Effective talent-identification procedures minimize the proportion of students whose subsequent performance indicates that they were mistakenly included in or excluded from the program. Classification errors occur when students who were predicted to excel subsequently do not excel or when students who were not predicted to excel do. Using a longitudinal ...

a limit- ing case of generalized LVQ (GLVQ) termed LVQ 2.1, learning from mistakes (LFM) and Robust Soft the generalization error at different sizes of the training set. In our findings, LFM achieves poor classification several important LVQ schemes: a limiting case of GLVQ, Learning From Mistakes (LFM) and Robust Soft LVQ

Accurate spectral signature classification is a crucial step in the nonimaging detection and recognition of spaceborne objects. In classical hyperspectral recognition applications, especially where linear mixing models are employed, signature classification accuracy depends on accurate spectral endmember discrimination. In selected target recognition (ATR) ...

Accurate spectral signature classification is key to the nonimaging detection and recognition of spaceborne objects. In classical hyperspectral recognition applications, signature classification accuracy depends on accurate spectral endmember determination [1]. However, in selected target recognition (ATR) applications, it is possible to circumvent the ...

... DTIC Online - This search queries the DTIC Online Public Web site. ... Aptitude, Error Tolerance, and Achievement Level as Factors of Performance in ...

BackgroundMolecular classification of tumors can be achieved by global gene expression profiling. Most machine learning classification algorithms furnish global error rates for the entire population. A few algorithms provide an estimate of probability of malignancy for each queried patient but the degree of ...

The proposed method can effectively identify a compact set of genes with high classification accuracy. This study also indicates that application of the Bayes error is a feasible and effective wayfor removing redundant genes in gene selection.

Following the 2002 Census of Agriculture, a Classification Error Study was conducted to estimate the number of operations misclassified (either as farms or non-farms) in the census. This was done by matching operations who reported in the Area Frame June ...

We investigate automatic identification of speculative language, or 'hedging', in scientific literature from the biomedical domain. Our contributions include a precise description of the task including annotation guidelines, theoretical analysis and discussion. We show that good agreement can be achieved using our guidelines and present a publicly available benchmark dataset ...

In this work an automated method is introduced to distinguish arteries from veins in eye fundus images. The main challenges of this task are the similarity of the two vessel types and the vast variability in different images. We assume that the vasculature is already extracted and represented by vessel segments. Based on local image features, vessel profile characteristics are extracted and used ...

Projection techniques are frequently used as the principal means for the implementation of feature extraction and dimensionality reduction for machine learning applications. A well established and broad class of such projection techniques is the projection pursuit (PP). Its core design parameter is a projection index, which is the driving force in obtaining the transformation function via ...

A novel 2D slice based fully automatic method for model based segmentation of the outer vessel wall of the common carotid artery in CTA data set is introduced. The method utilizes a lumen segmentation and AdaBoost, a fast and robust machine learning algorithm, to initially classify (mark) regions outside and inside the vessel wall using the distance from the lumen and intensity profiles sampled ...

Mosses and lichens have an important role in biomonitoring. The objective of this study is to develop a neural network model to classify these plants according to geographical origin. A three-layer feed-forward neural network was used. The activities of radionuclides ((226)Ra, (238)U, (235)U, (40)K, (232)Th, (134)Cs, (137)Cs and (7)Be) detected in plant samples by gamma-ray spectrometry were used ...

Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available algorithms. However, few efforts have been made to extend SVMs to cover the specific requirements of hyperspectral image classification, for example, by ...

Much of the filter design work that has been performed to date for filter SLMs with both constrained

... 15. SUBJECT TERMS Quantum Information Science, Quantum Algorithms, Quantum Cryptography 16. SECURITY CLASSIFICATION OF: 17. ...

... such as merging and spacing, Standard Terminal ... initiati es to keep aircraft spaced, either by ... Using knowledge exploration tools to study airspace ...

MODIS Reports Link � MODIS Science Team Meetings Link ... combination and adjacent temporal deduction can reduce cloud obscuration and classification errors ...

... Page 22. 16 classification boundary of two groups of "fuzzy" response ... space') that is capable of diagnosing cognitive errors. Rule ...

... Complexity Measure of ECG signals for the classification of cardiac arrhythmia's. ... performance, especially for SR signals, and an overall error rate of ...

... AUTOMATIC, GRAPHICAL USER INTERFACE, LABELS, TARGET CLASSIFICATION, ERRORS, COSTS, PROBABILITY, TOOLS, INTERFACES. ...

The exhaustive quality control is becoming very important in the world's globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole ...

Objective: To describe a classification of errors and to assess the feasibility and acceptability of a method for recording staff reported errors in general practice. Design: An iterative process in a pilot practice was used to develop a classification of errors. This was incorporated in an ...

A framework which allows for the direct comparison of alternate approaches to automatic target recognition (ATR) from synthetic aperture radar (SAR) images is described and applied to variants of several ATR algorithms. This framework allows comparisons to be made on an even footing while minimizing the impact of implementation details and accounts for variation in image sizes, in angular ...

Data mining involves the discovery and fusion of features from large databases to establish minimal probability of error (MPE) decision and estimation models. Our approach combines a weighted nearest neighbor (WNN) decision model for classification and estimation with genetic algorithms (GA) for feature discovery and model optimization. The WNN model is ...

We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings in a brain computer interface (BCI) task. The technique is based on an adaptive time-frequency analysis of EEG signals computed using local discriminant bases (LDB) derived from local cosine packets (LCP). In an offline step, the EEG data obtained from the C(3)/C(4) ...

Image preprocessing is useful in helping to identify `spectral response patterns' for certain types of image classification problems. The common artifacts in remotely sensed images are caused by the blurring due to the optics of the image gathering device, illumination variations, and the radiative transfer of the atmosphere. The Multi-Scale Retinex (MSR) image enhancement ...

Systems and methods for enhancing the accuracy of classifying a measurement by providing an artificial class. Seizure prediction systems may employ a classification system including an artificial class and a user interface for signaling uncertainty in classification when a measurement is classified in the artificial class.

... AER Actual Error Rate AUC Area Under the ROC Curve pj Class Prevalence cij Classification Cost C Classification Cost Matrix [C]ij = cij �ij(A ...

There are a variety of factors that make the pediatric population more susceptible to medication errors and potential complications resulting from medication administration including the availability of different dosage forms of the same medication, incorrect dosing, lack of standardized dosing regimen, and organ system maturity. A systematic literature search on medication ...

Diabetes-related medical errors in outpatient practice are common and costly. This study attempts to accurately identify, classify, and interpret patterns of diabetes-related medical errors in primary care settings using diagnostic, laboratory, and pharma...

Inaccuracies in dose assignments can lead to misclassification in epidemiological studies. The extent of this misclassification is examined for different error functions, classification intervals, and actual dose distributions. The error function model is...

BackgroundDetailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination with methods for fluorescent tagging, is the most suitable current method for proteome-wide determination of subcellular location. Previous work has shown that neural network classifiers can distinguish all major protein ...

The Puget Sound Lidar Consortium, working with TerraPoint, LLC, has procured a large volume of ALSM (topographic lidar) data for scientific research. Research-grade ALSM data can be characterized by their completeness, density, and accuracy. Complete data include-at a minimum-X, Y, Z, time, and classification (ground, vegetation, structure, blunder) for each laser reflection. ...

Minimal Achievable Error in the LED problem Xavier Llora and David E. Goldberg IlliGAL Report No Achievable Error in the LED problem Xavier Llora # and David E. Goldberg Illinois Genetic Algorithms a theoretical model to predict the minimal achievable error, given a noise ...

Least Development Countries (LDC) like Bangladesh, whose 25 % revenue earning is achieved from Textile export, requires producing less defective textile for minimizing production cost and time. Inspection processes done on these industries are mostly manual and time consuming. To reduce error on identifying fabric defects requires more automotive and ...

In rapidly changing peri-urban environments where biophysical and socio-economic processes lead to spatial fragmentation of agricultural land, remote sensing offers an efficient tool to collect land cover/land use (LCLU) data for decision-making. Compared to traditional pixel-based approaches, remote sensing with object-based classification methods is reported to ...

This paper presents a novel algorithm for Nearest Neighbor (NN) classifier. NN classification is a well-known method of pattern classification having the following properties: * it performs maximum-margin classification and achieves less than twice the ideal Bayesian error, * it does not ...

BackgroundTranscription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to ...

There are many different types of errors in neuronavigation, and the reasons and results of these errors are complex. For a neurosurgeon using the neuronavigation system, it is important to have a clear understanding of when an error may occur, what the magnitude of it is, and how to avoid it or reduce its influence on the final ...

... result set, which helps a lot in achieving high classification accuracy. By introducing several novel search strategies and pruning methods into the ...

Discrimination of pigmented and vascular lesions in skin can be difficult due to factors such as size, subungual location, and the nature of lesions containing both melanin and vascularity. Misdiagnosis may lead to precancerous or cancerous lesions not receiving proper medical care. To aid in the rapid and accurate diagnosis of such pathologies, we develop a photoacoustic system to determine the ...

Discrimination of pigmented and vascular lesions in skin can be difficult due to factors such as size, subungual location, and the nature of lesions containing both melanin and vascularity. Misdiagnosis may lead to precancerous or cancerous lesions not receiving proper medical care. To aid in the rapid and accurate diagnosis of such pathologies, we develop a photoacoustic system to determine the ...

Through systematically analysises of existing multi-class SVMs (M-SVMs) methods, it is shown that hierarchy multi-class SVMs (H-SVMs) can be relatively effective. Further analysis shown that existing methods that measure separability between different classes are not suitable for kernel feature space. A new method is presented for separability measure in feature space based on the characters of ...

A measuring strategy for the evaluation of a seven parameters colorimetric test using a computer screen photo-assisted technique (CSPT) is demonstrated. CSPT is a versatile approach aimed at point of care or home tests that uses regular computer sets and web cameras as the whole instrument. Issues such as the stability and the equivalency on different platforms of the determinations have been ...

The combination of (1)H NMR fingerprinting of lipids from gilthead sea bream (Sparus aurata) with nonsupervised and supervised multivariate analysis was applied to differentiate wild and farmed fish and to classify farmed specimen according to their areas of production belonging to the Mediterranean basin. Principal component analysis (PCA) applied on processed (1)H NMR profiles made a clear ...

We present preliminary results of a study to evaluate which is the more fundamental relationship in galaxy morphology within galaxy clusters -- the local density of the galaxy environment or the projected distance of the galaxy from the cluster center. The completed sample will contain 107 Abell clusters, including all northern Abell clusters with distance class <= 4 and richness class >= 1 ...

The accurate classification of everyday movements from accelerometry data will provide a significant step towards the development of effective ambulatory monitoring systems for falls detection and prediction. The search continues for optimal front-end processing methods for use in accelerometry systems. Here, we propose a novel set of time domain features, which ...

Abstract Microarray analysis promises to detect variations in gene expressions, and changes in the transcription rates of an entire genome in vivo. Microarray gene expression profiles indicate the relative abundance of mRNA corresponding to the genes. The selection of relevant genes from microarray data poses a formidable challenge to researchers due to the high-dimensionality of features, ...

We present a systematic study on gender classification with automatically detected and aligned faces. We experimented with 120 combinations of automatic face detection, face alignment and gender classification. One of the findings was that the automatic face alignment methods did not increase the gender classification rates. However, ...

The k-nearest neighbor method is a classifier based on the evaluation of the distances to each pattern in the training set. The edited version of this method consists of the application of this classifier with a subset of the complete training set in which some of the training patterns are excluded, in order to reduce the classification error rate. In ...

This paper is concerned with the selection of a generative model for supervised classification. Classical criteria for model selection assess the fit of a model rather than its ability to produce a low classification error rate. A new criterion, the Bayesian Entropy Criterion (BEC), is proposed. This criterion takes into account the ...

In previous work, it has been experimentally shown that the implementation of Error Correcting Output Coding (ECOC) classification methods with an ensemble of parallel and independent non linear dichotomizers (ECOC PND) outperforms the implementation with a single monolithic multi layer perceptron (ECOC MLP). This result was ascribed to the higher ...

. TheIndividual Classification Success Index (ICSI) is introduced which accounts for the classification of this index is given as ICSI = 1 - (Errorof Omission % +Error of Commission %) (16) Often, in producing maps for each class;errors of commission per class; errors of omission per ...

is an integer equal to the number of times the i-th edge is used in forming the path. Thus, if we arrange. Huang 2009 Software Error Detection - Chapter 2 146 CW+C error classification method (contin - Chapter 2 147 CW+C error classification method (contin'd) Thus we have a canonical ...

Novel model based features are introduced in the discrimination of motor imagery tasks using human scalp electroencephalography (EEG) towards the development of Brain Computer Interfaces (BCI). We have acquired human scalp EEG under open-loop and feedback conditions in response to cue-based motor imagery tasks. EEG signals, transformed into frequency specific bands such as mu, beta and movement ...

Background: Reported error rates for initial clinical diagnosis in parkinsonian disorders can reach up to 35%. Reducing this initial error rate is an important research goal. The objective of this work is to evaluate the ability of an automated MR-based classification technique in the differential diagnosis of Parkinson's disease (PD), ...

channel error resilience for a specific channel bit error rate (BER), but higher resilience can still be achieved by decreasing the sensitivity ...

We compare two strategies for training connectionist (as well as non-connectionist) models for statistical pattern recognition. The probabilistic strategy is based on the notion that Bayesian discrimination (i.e., optimal classification) is achieved when the classifier learns the a posteriori class distributions of the random feature vector. The ...

We participated in the i2b2 smoking status classification challenge task. The purpose of this task was to evaluate the ability of systems to automatically identify patient smoking status from discharge summaries. Our submission included several techniques that we compared and studied, including hot-spot identification, zero-vector filtering, inverse class frequency weighting, ...

For quantitative analysis of histopathological images, such as the lymphoma grading systems, quantification of features is usually carried out on single cells before categorizing them by classification algorithms. To this end, we propose an integrated framework consisting of a novel supervised cell-image segmentation algorithm and a new touching-cell splitting method. For the ...

classification using velocity estimates from SLAM with GO should be feasible. B. Thresholding ClassificationAchieving Undelayed Initialization in Monocular SLAM with Generalized Objects Using Velocity classification method using the velocity estimates directly from SLAM with GO. Compared to the existing approach

Multivariate machine learning techniques provide an alternative to the rapidity gap method for event-by-event identification and classification of diffraction in hadron-hadron collisions. Traditionally, such methods assign each event exclusively to a single class producing classification errors in overlap regions of data space. As an ...

... TITLE (Inciuae Securit'y Classification) POLAND AND THE UNITED STATES: ACHIEVING AMERICAN NATIONA'L INTERESTS IN EASTERN ...

... On classified documents, enter the title classification in ... Age � Gender � Race/ethnicity ... predicts youth educational aspirations and achievement. ...

... The major achievements of the Phase I study include: (1) Establishment of a Gaussian Potential Function Network (GPFN) architecture that allows ...

... c' I on Educational Achievement By Cm.. ... Educational information about recruits was evaluated for its poten- tial contribution to airman classification. ...

... the African continent stares at virtual extinction ... In sum, rigid academic classification should not ... criminals � a principal achievement of monarchs in ...

... and Detection of Unknown Formats Using Artificial Neural Networks. ... of two different neural network types, and ... of the neural networks and achieve ...

We propose a new discriminative learning framework, called soft margin estimation (SME), for estimating parameters of continuous density hidden Markov models. The proposed method makes direct usage of the successful ideas of soft margin in support vector machines to improve generalization capability, and of decision feedback learning in minimum classification ...

The current investigation identified characteristics that discriminated authentic dyslexia from its simulation using measures common to postsecondary learning disability evaluations. Analyses revealed accurate simulation on most achievement measures but inaccurate feigning on neurolinguistic processing measures, speed on timed tasks, and error quantity. ...

A system is presented for measuring relative volumes of histologic compartments in microscopic slides. The computer program for that system was written by us as an MS windows 3.x application. The applied method of measurements, based on the "point-counting" technique, requires the recognition of the compartments, corresponding to the selected points of an image. The neural network was employed to ...

With the development of medical science, three-dimensional ultrasound and color power Doppler tomography shooting placenta is widely used. To determine whether the fetus's development is abnormal or not is mainly through the analysis of the capillary's distribution of the obtained images which are shot by the Doppler scanner. In this classification process, we will adopt ...

The classification of targets viewed by a pushbroom type multiple band spectral scanner by algorithms suitable for implementation in high speed online digital circuits is considered. A class of algorithms suitable for use with a pipelined classifier is in...

A SAR image segmentation by the gray level only is discussed. Classification errors of SAR images, the existence of histogram valleys suitable for classification, and a procedure for multilevel thresholdings of SAR images are discussed. Not all images, wh...

Impacts of Patch Size and Landscape Heterogeneity on Thematic Image Classification Accuracy. Currently, most thematic accuracy assessments of classified remotely sensed images oily account for errors between the various classes employed, at particular pixels of interest, thu...

This thesis presents a prototype vehicle acoustic signal classification system with low classification error and short processing delay. To analyze the spectrum of the vehicle acoustic signal, we adopt biologically motivated feature extraction models - co...

Standard classification algorithms aim to minimize the probability of making an incorrect classification. In many important applications, however, some kinds of errors are more important than others. In this report we review cost-sensitive extensions of s...

We propose an adaptive classification method for the Brain Computer Interfaces (BCI) which uses Interaction Error Potentials (IErrPs) as a reinforcement signal and adapts the classifier parameters when an error is detected. We analyze the quality of the proposed approach in relation to the misclassification of the IErrPs. In addition ...

Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of ...

Discrimination of material based on elemental composition was achieved within a compositional data (CoDa) analysis framework in a form appropriate for use in forensic science. The methods were carried out on example data from New Zealand nephrite. We have achieved good separation of the in situ outcrops of nephrite from within a well-defined area. The most ...

Jun 16, 2011 ... It has not been subjected to OCR (Optical Character Recognition) and therefore does not have any errors in the text. However it is a larger ...

were influenced by classwide correlations with body size. However, there were errors of classification

... versus post-processing SNR, comparing performance with a ± 100 aspect error at various ... 4. Filter (in range) the subtracted files to further reduce ...

task, evaluation of a classification error prediction algorithm and development of scanner ..... A subsequent document, Technique Interchange Plan-Part II, ...

... provide imperfect target classification and are prone to false positive and false negative errors when attempting to identify targets in operational ...

... Monterey, CA 939140 Takoma Park, MD 20012 Dr. Joseph Psotka ... School of Education Smithsonian Institution University of Massachusetts ...

belonging to a finite set, whose value is determined by x, that is there exists a function y = y(x). We here consider ...... Thus, to provide a fair comparison with the ...

... 4. TITLE AND SUBTITLE ... Each type of reliability estimate treats measurement error as a ... Indeed, the face validity of hands-on testing is one of the ...

OBJECTIVE: To validate a classification sheet for medication errors associated with antineoplastic medication. METHOD: Prospective study. A data sheet was designed based on ASHP's classification. Two observers reviewed the treatment prescribed for chemotherapy from the Haematology Department during a month and they classified the ...

The authors have developed a self-organized neural network based method that concurrently detects segmentation errors and performs character recognition. This method utilizes a two-pass classification scheme. A page of machine printed text is segmented, a...

When training an artificial neural network (ANN) for classification using backpropagation of error, the weights are usually updated by minimizing the sum-squared error on the training set. As training ensues, overtraining may be observed as the network be...

This project will implement �impact� analysis and explanation services over NCIt in the NCI version of Protégé to help identify modeling errors. This is important because it is difficult to identify source of errors with current classifiers.

Human error has been implicated in 70 to 80% of all civil and military aviation accidents. Yet, most accident reporting systems are not designed around any theoretical framework of human error. As a result, most accident databases are not conducive to a t...

Jun 16, 2011 ... Description: Coregistration and classification errors seriously compromise per- pixel estimates of land cover change. A more robust estimation ...

This paper introduces a probabilistic model that is capable of diagnosing and classifying cognitive errors in a general problem-solving domain. The model is different from the usual deterministic strategies common in the area of artificial intelligence be...

A categorized data base of software errors which were discovered during the various stages of development and operational use of the Deep Space Network DSN/Mark 3 System was developed. A study team identified several existing error classification schemes ...

In the face of global change, concepts for sustainable land management are increasingly requested, among others to cope with the rapidly increasing energy demand. High resolution land use classifications can contribute spatially explicit information suitable for land use planning. In this study, the coverage of cereal crops was derived for two regions in Baden-Wuerttemberg and ...

Characterizing and classifying regularities in protein structure is an important element in uncovering the mechanisms that regulate protein structure, function and evolution. Recent research concentrates on analysis of structural motifs that can be used to describe larger, fold-sized structures based on homologous primary sequences. At the same time, accuracy of secondary protein structure ...

Intestinal drug absorption in humans is a central topic in drug discovery. In this study, we use a broad selection of machine learning and statistical methods for the classification and numerical prediction of this key end point. Our data set is based on a selection of 458 small druglike compounds with FDA approval. Using easily available tools, we calculated one- to ...

This paper presents a method for classification of structural brain magnetic resonance (MR) images, by using a combination of deformation-based morphometry and machine learning methods. A morphological representation of the anatomy of interest is first obtained using a high-dimensional mass-preserving template warping method, which results in tissue density maps that ...

Abstract Purpose: To provide a comprehensive review of the classification of surgical errors as well as general measures to detect and prevent their occurrence. Materials and Methods: Search in PubMed, Medline, and Cochrane library with combination of the key words: Endoscopy or surgical procedures, minimally invasive, and medical ...

In this thesis we study the performance of Single Photon Emission Computed Tomography using concepts and techniques of information theory. Two specific tomographic tasks are considered: image reconstruction and image feature classification. For the image reconstruction problem we derive a necessary and sufficient condition for an aperture to be information optimal. The ...

The paper describes the way and results of human reliability data analysis collected as a part of the Bohunice Simulator Data Collection Project (BSDCP), which was performed by VUJE Trnava, Inc. with funding support from the U.S. DOE, National Nuclear Security Administration. The goal of the project was to create a methodology for simulator data collection and analysis to support activities in ...

The mechanization of a gimballess inertial system for space navigation is considered. An error analysis reveals that the errors contain both sinusoidal and diverging components. Error damping methods are proposed and it is shown that damping can be achiev...

A concatenated coded modulation scheme for error control in data communications is presented. The scheme is achieved by concatenating a Reed-Solomon outer code and a bandwidth efficient block inner code for M-ary PSK modulation. Error performance of the s...

A concatenated coded modulation scheme for error control in data communications is described. The scheme is achieved by concatenating a Reed-Solomon outer code and a bandwidth efficient block inner code for M-ary PSK modulation. Error performance of the s...

The primary objective of this effort is to determine whether a one mile flight technical error is achievable in enroute and terminal area operations using various types of airborne RNAV equipment. Flight technical error (FTE) is evaluated using both contr...

In this paper we propose a method for construction of feed-forward neural classifiers based on regularization and adaptive architectures. Using a penalized maximum likelihood scheme, we derive a modified form of the entropic error measure and an algebraic estimate of the test error. In conjunction with optimal brain damage pruning, a test ...

As frontline clinicians, nurses play a critical role in mitigating patient harm, recovery from health care errors, and overall improvement of patient safety. This cross-sectional study asked nurse respondents to make judgments about the classification and severity of errors in 4 clinical vignettes. Our results showed that agreement ...

This article proposes a multilevel model for the assessment of school effectiveness where the intake achievement is a predictor and the response variable is the achievement in the subsequent periods. The achievement is a latent variable that can be estimated on the basis of an item response theory model and hence subject to measurement ...

A method is presented for achieving a dramatic improvement in phase tracking ... window can be made so narrow as to achieve a phase error variance of 1.503. ...

In this paper, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target in a new frame, each target candidate is sparsely represented in the space spanned by ...

For children and adolescents with cerebral palsy (CP) classified as Gross Motor Function Classification System (GMFCS) level III there is no running-based field test available to assess their cardiorespiratory fitness. The current study investigated whether a shuttle run test can be reliably (test-retest) performed in a group of children with spastic diplegia (eight male, five ...

In this paper, we present a novel approach of face identification by formulating the pattern recognition problem in terms of linear regression. Using a fundamental concept that patterns from a single-object class lie on a linear subspace, we develop a linear model representing a probe image as a linear combination of class-specific galleries. The inverse problem is solved using the least-squares ...

For children and adolescents with cerebral palsy (CP) classified as Gross Motor Function Classification System (GMFCS) level III there is no running-based field test available to assess their cardiorespiratory fitness. The current study investigated whether a shuttle run test can be reliably (test-retest) performed in a group of children with spastic diplegia (eight male, five ...

This paper describes the design of a PC-based real-time machine vision system for detecting and classifying small marine organisms like fish eggs and planktons in flowing water. The system is called the Real-time FLow Imaging and Classification System, or ReFLICS for short, and it will automate the task of visually counting and classifying fish egg samples which is currently ...

With the emergence of a global economy, companies are more than ever pressured for improved efficiency. Int he transportation industry there is a growing need for better tracking of the status of containers in transit. This would lead to improved handling operation, reduce the number of errors, increase the throughput and enable the use of electronic data interchange (EDI). As ...

This paper presents a novel image compression scheme based on the perceptual classification of image patterns in the Discrete Hermite Transform (DHT) domain over a roughly hexagonal sampling lattice. The DHT analyzes a signal through a set of binomial filters which approximate the Gaussian derivatives with the advantage that they are computed efficiently. In order to obtain ...

Most decision tree classifiers are designed to keep class histograms for single attributes, and to select a particular attribute for the next split using said histograms. In this paper, we propose a technique where, by keeping histograms on attribute pairs, we achieve (i) a significant speed-up over traditional classifiers based on single attribute splitting, and (ii) the ...

The artificial neural network (ANN) data analysis method was used to recognize and classify soils of an unknown geographic origin. A total of 103 soil samples were differentiated into classes according to the regions in Serbia and Montenegro from which they were collected. Their radionuclide (226Ra, 238U, 235U, 40K, 134Cs, 137Cs, 232Th, and 7Be) activities detected by gamma-ray spectrometry were ...

Jun 16, 2011... and the impact of these errors on the efficiency of rough mills. ... How these goals can be achieved in practice also is discussed. ...

... quality of computerized adaptive testing as an ... An adaptive achievement test for teaching signed-numbers ... be needed in future computerized tests. ...

Establishing control processes to achieve quality, error-free software; Using estimating models as a management tool throughout the software development ...

The Communications Architectures and Research Section (332) has a heritage of significant achievements in space communications, which include error ...

... at the decoder to determine the error pattern. ... Knowing y and the syndrome of x, the ... structured segmentation that achieve the correct asymptotic rate ...

The reduction of noise and vibration is achieved by application of a predesigned parabolic function of transmission errors that is able to absorb a ...

... The primary objective of the JMAPS mission is the generation of an astrometric star ... Distribution Statement : APPROVED FOR PUBLIC RELEASE. ...

This study presents a system for classifying echolocation clicks of six species of odontocetes in the Southern California Bight: Visually confirmed bottlenose dolphins, short- and long-beaked common dolphins, Pacific white-sided dolphins, Risso's dolphins, and presumed Cuvier's beaked whales. Echolocation clicks are represented by cepstral feature vectors that are classified by Gaussian mixture ...

We propose a novel topology preserving self-organized map (SOM) classifier with transductive confidence machine (TPSOM-TCM). Typically, SOM acts as a dimension reduction tool for mapping training samples from a high-dimensional input space onto a neuron grid. However, current SOM-based classifiers can not provide degrees of classification reliability for new unlabeled samples ...

We developed a simple, tiny setup with only a few optical devices for fast and sensitive identification of DNA with a short-pulse semiconductor laser operating at 20 MHz. In combination with newly synthesized fluorescent dyes (rhodamine derivatives) which exhibit high fluorescence quantum efficiencies and distinct fluorescence lifetimes at semiconductor laser excitation wavelength a sensitive ...

Unlike sub-optimal prescribing, rational prescribing, coupled with certain indicators, is associated with improved safety in drug use in terms of selecting appropriate drug for prescribing, better quality of life for patients and cost-effective care. Medication prescribing is a relatively unexplored area of research in Saudi Arabia and until now most studies have been in the secondary and tertiary ...

PEAK # Features SN SP Test Error No 5 0.8697 0.669 0.2132 No 10 0.9073 0.9039 0.0941 No 15 0.9298 0.9537 0.0603 No 20 0.9499 0.9502 0.05 Yes 5 0.9123 0.726 0.1647 Yes 10 0.9424 0.9644 0.0485 Yes 15 0.9474 0.968 0.0441 Yes 20 0.9348 0.9751 0.0485 Kern S,H.R.H.M. 2001.

Due to the increased threats of chemical and biological weapons of mass destruction (WMD) by international terrorist organizations, a significant effort is underway to develop tools that can be used to detect and effectively combat biochemical warfare. Furthermore, recent events have highlighted awareness that chemical and biological agents (CBAs) may become the preferred, cheap alternative WMD, ...

This article presents some efficient training algorithms, based on first-order, second-order, and conjugate gradient optimization methods, for a class of convolutional neural networks (CoNNs), known as shunting inhibitory convolution neural networks. Furthermore, a new hybrid method is proposed, which is derived from the principles of Quickprop, Rprop, SuperSAB, and least squares (LS). ...

Pre-germination is one of many serious degradations to barley when used for malting. A pre-germinated barley kernel can under certain conditions not re-germinate and is reduced to animal feed of lower quality. Identifying pre-germination at an early stage is therefore essential in order to segregate the barley kernels into low or high quality. Current standard methods to quantify pre-germinated ...

The objective of this paper is to develop novel classification structures for military targets detection and recognition by employing different fusion techniques. In real applications, the great diversity of materials in the background areas and the similarity between the background and target signatures result in high false alarm rates and large miss ...

A categorized data base of software errors which were discovered during the various stages of development and operational use of the Deep Space Network DSN/Mark 3 System was developed. A study team identified several existing error classification schemes (taxonomies), prepared a detailed annotated bibliography of the ...

Using convex optimization, we propose entanglement-assisted quantum error-correction procedures that are optimized for given noise channels. We demonstrate through numerical examples that such an optimized error-correction method achieves higher channel fidelities than existing methods. This improved performance, which leads to perfect ...

Using convex optimization, we propose entanglement-assisted quantum error-correction procedures that are optimized for given noise channels. We demonstrate through numerical examples that such an optimized error-correction method achieves higher channel fidelities than existing methods. This improved performance, which leads to perfect ...

In production agriculture, savings in herbicides can be achieved if weeds can be discriminated from crop, allowing the targeting of weed control to weed-infested areas only. Previous studies demonstrated the potential of ultraviolet (UV) induced fluorescence to discriminate corn from weeds and recently, robust models have been obtained for the discrimination between monocots ...

The Human Factors Analysis and Classification System (HFACS) is a general human error framework originally developed and tested within the U.S. military as a tool for investigating and analyzing the human causes of aviation accidents. Based upon Reasons (...

This thesis is concerned with the effect of classification error on contingency tables being analyzed with hierarchical log-linear models (independence in an I x J table is a particular hierarchical log-linear model). Hierarchical log-linear models provid...

In this paper, we describe an incrementally generated fuzzy neural network (FNN) for intelligent data processing. This FNN combines the features of initial fuzzy model self-generation, fast input selection, partition validation, parameter optimization and rule-base simplification. A small FNN is created from scratch -- there is no need to specify the initial network architecture, initial ...

This study was designed to determine whether various techniques for achieving rectangular collimation generate different numbers and types of errors and remakes and to determine whether operator skill level influences errors and remakes. Eighteen students exposed full-mouth series of radiographs on manikins with the use of six ...

Classification trees were tested as a potential methodology for increasing the thematic detail of forest maps for the Indiana Gap Analysis Project. The study area used to test the methodology was Jefferson Proving Ground in southeastern Indiana. Multi-temporal Landsat TM data were integrated with digitized elevation and soils data using the classification ...

Neuroblastoma is a cancer of the nervous system and one of the most common tumors in children. In clinical practice, pathologists examine the haematoxylin and eosin (H&E) stained tissue slides under the microscope for the diagnosis. According to the International Neuroblastoma Classification System, neuroblastoma tumors are categorized into favorable and unfavorable ...

In this paper we explore the use of histogram features extracted from 3D point clouds of human subjects for gender classification. Experiments are conducted using point clouds drawn from the CAESAR anthropometric database provided by the Air Force Research Laboratory (AFRL) Human Effectiveness Directorate and SAE International. This database contains approximately 4400 high ...

The overall aim of our research is to develop methods for a monitoring system to be used at neonatal intensive care units. When monitoring a baby, a range of different types of background activity needs to be considered. In this work, we have developed a scheme for automatic classification of background EEG activity in newborn babies. EEG from six full-term babies who were ...

Many different kinds of physics are used in sensors that produce time-domain signals, such as ultrasonics, acoustics, seismology, and electromagnetics. The waveforms generated by these sensors are used to measure events or detect flaws in applications ranging from industrial to medical and defense-related domains. Interpreting the signals is challenging because of the complicated physics of the ...

Most of the traditional evaluation criteria of object classification are based on the error rate, assuming that the costs of different errors are equal. However, the subjective evaluation of the human vision system on such misclassification errors may be unequal. How do we design proper performance evaluation ...

A variant of nearest-neighbor (NN) pattern classification and supervised learning by learning vector quantization (LVQ) is described. The decision surface mapping method (DSM) is a fast supervised learning algorithm and is a member of the LVQ family of algorithms. A relatively small number of prototypes are selected from a training set of correctly classified samples. The ...

Errors in image interpretation are a common problem in diagnostic radiology. Although many published articles provide trainees with the means to correctly interpret imaging studies, they do not provide a framework for understanding why and how errors occur. In this article, we propose a classification system that allows categorization ...

The Outpatient Code Editor (OCE) with Ambulatory Classification (APC) program edits patient data to help identify possible errors in coding, and assigns Ambulatory Payment classification numbers based on Healthcare Common Procedure Coding System (HCPCS) c...

Two-class supervised learning in the context of a classifier ensemble may be formulated as learning an incompletely specified Boolean function, and the associated Walsh coefficients can be estimated without the knowledge of the unspecified patterns. Using an extended version of the Tumer-Ghosh model, the relationship between added classification error and ...

This research evaluated 24 predictors, including experimental tests administered in a technical classification assessment center (TCAC), biographical variables, and Armed Services Vocational Aptitude Battery (ASVAB) tests, for classifying personnel for ge...

An opto-mechanical scanning technique and initial experiments are described in which characteristics of undirectional resolution produced by one-dimensional imaging is used both for detection and geometric classification of linear arrays. Classification is achieved by angular identification of linear arrays.

As a seamless, continuation of the previous SWAMSI grant, the objective continues to be the achievement of robust multi-static detection and classification of proud- and buried seabed objects using cooperative networks of autonomous vehicles with acoustic...

The primary objective of the research and development was to achieve classification and feature extraction from high resolution digital terrain elevation (DTE) data and corresponding fine resolution SAR imagery generated from interferometric synthetic ape...

The author has identified the following significant results. ECHO successfully exploits the redundancy of states characteristics of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, ...

pieces, wrong column/row information, and omitting cells/whole tables etc. Some of the errors canImproving the Table Boundary Detection in PDFs by Fixing the Sequence Error of the Sparse Lines- ment storage, classification and retrieval. Table, a ubiqui- tous document component, becomes

dichotomizers (ECOC PND). Moreover, the ex- perimentation analyzes the relationship between the architecture of independent parallel dichotomizers are better suited for implementing ECOC classification methods output errors and the diversity between base learners. Key words: Error Correcting Output Coding, ECOC