Results 1 - 10
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10,442
On combining classifiers
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1998
"... We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental ..."
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Cited by 1420 (33 self)
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We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision
On the Nonlinearity of Pattern Classifiers
- Proc. of the 13th ICPR
, 1996
"... This paper presents a novel approach to the analysis of the overtraining phenomenon in pattern classifiers. A nonlinearity measure N is introduced which relates the shape of the classification function to the generalization capability of a classifier. Experiments using the k-nearest neighbour rule, ..."
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Cited by 10 (1 self)
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This paper presents a novel approach to the analysis of the overtraining phenomenon in pattern classifiers. A nonlinearity measure N is introduced which relates the shape of the classification function to the generalization capability of a classifier. Experiments using the k-nearest neighbour rule
The convergence of adaptive pattern classifiers
"... Synopsis Whilst the convergence of adaptive-threshold-logic elements may be dealt with quite simply when they are used for pattern recognition, the behaviour of such devices is far more complex and less amenable to analysis when they form the variable element of an adaptive control system. This is b ..."
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. This is because they have the dual control problem of identifying their environment by classification whilst controlling it, and performance feedback is both relative and global. This paper considers five problem areas in the utilization of pattern classifying control systems: the derivation of performance
Comparison of algorithms that select features for pattern classifiers
- Pattern Recognition
, 2000
"... A comparative study of algorithms for large-scale feature selection (where the number of features is over 50) is carried out. In the study, the goodness of a feature subset is measured by leave-one-out correct-classi"cation rate of a nearestneighbor (1-NN) classi"er and many practical prob ..."
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Cited by 207 (0 self)
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-scale problems and genetic algorithms are suitable for large-scale problems. � 1999 Pattern Recognition Society. Published
A training algorithm for optimal margin classifiers
- PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
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Cited by 1865 (43 self)
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A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters
0A Survey of Pattern Classifier Research
, 2004
"... This paper is a survey of research on pattern classifier. In particular, it emphasizes on the different types of pattern classifiers and their performance factors. Pattern classifiers use the algorithms of pattern recognition to classify various input classes into their respective categories. Recent ..."
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This paper is a survey of research on pattern classifier. In particular, it emphasizes on the different types of pattern classifiers and their performance factors. Pattern classifiers use the algorithms of pattern recognition to classify various input classes into their respective categories
Statistical pattern recognition: A review
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2000
"... The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques ..."
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Cited by 1035 (30 self)
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, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved
Combining Pattern ClassifiersCombining Pattern Classifiers Methods and Algorithms
"... Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States ..."
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Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee
Theoretic performance of genetic pattern classifier
- Journal of The Franklin Institute
, 1999
"... An investigation is carried out to formulate some theoretical results regarding the behavior of a genetic-algorithm-based pattern classification methodology, for an infinitely large number of training data points n, inanN-dimensional space R�. It is proved that for nPR, and for a sufficiently large ..."
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Cited by 3 (0 self)
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An investigation is carried out to formulate some theoretical results regarding the behavior of a genetic-algorithm-based pattern classification methodology, for an infinitely large number of training data points n, inanN-dimensional space R�. It is proved that for nPR, and for a sufficiently large
Results 1 - 10
of
10,442