Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 37
Sivu 7
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig . 1.4 , would employ R dis ...
... classifier method will produce a more detailed functional block diagram of the basic model for a pattern classifier discussed in Sec . 1.2 . Our discriminant- function pattern classifier , illustrated in Fig . 1.4 , would employ R dis ...
Sivu 9
... classifier whose discriminant functions can be obtained by training is called a trainable pattern classifier . tienn 1 datace discriminants , not any date 1.7 Training methods " In this book we shall discuss examples of two types of ...
... classifier whose discriminant functions can be obtained by training is called a trainable pattern classifier . tienn 1 datace discriminants , not any date 1.7 Training methods " In this book we shall discuss examples of two types of ...
Sivu 24
... classifier with respect to point sets . Suppose we are given R finite point sets P1 , P2 , i = 1 , · " ... " PR . " For each R , let the ith point set consist of the L points P , ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the Euclidean ...
... classifier with respect to point sets . Suppose we are given R finite point sets P1 , P2 , i = 1 , · " ... " PR . " For each R , let the ith point set consist of the L points P , ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the Euclidean ...
Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix 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 Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |