Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu viii
... measurements or properties on which recognition is based is one of the most important problems in pattern recognition . Yet while there have been many schemes advanced for testing the worth of already selected measurements , the author ...
... measurements or properties on which recognition is based is one of the most important problems in pattern recognition . Yet while there have been many schemes advanced for testing the worth of already selected measurements , the author ...
Sivu 4
... measurements . At worst this selection process is guided solely by the designer's intuitive ideas about which measurements play an important role in the classification to be performed . At best the process can make use of known ...
... measurements . At worst this selection process is guided solely by the designer's intuitive ideas about which measurements play an important role in the classification to be performed . At best the process can make use of known ...
Sivu 12
... measurements from a larger pool of measurements . Block , Nilsson , and Duda 12 describe a method for determining features of pat- terns . Some specific examples of measurement devices for optical charac- ter recognition are discussed ...
... measurements from a larger pool of measurements . Block , Nilsson , and Duda 12 describe a method for determining features of pat- terns . Some specific examples of measurement devices for optical charac- ter recognition are discussed ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |