Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 89
... reduced training sequence Sy and the reduced weight - vector sequences Sŵ ,, SŴR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ of ...
... reduced training sequence Sy and the reduced weight - vector sequences Sŵ ,, SŴR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ of ...
Sivu 90
... reduced weight - vector sequences , Sŵ ,, ... , SŴR . Let V be the kth member of the sequence Sy . If the respective kth mem- bers of Sŵ1 , . , Sŵ are given by Ŵ1 ( * ) , ŴR ) then V is deter- mined as follows : The first block of D ...
... reduced weight - vector sequences , Sŵ ,, ... , SŴR . Let V be the kth member of the sequence Sy . If the respective kth mem- bers of Sŵ1 , . , Sŵ are given by Ŵ1 ( * ) , ŴR ) then V is deter- mined as follows : The first block of D ...
Sivu 91
... reduced training sequence Sy then creates a reduced weight - vector sequence Sŵ such that · k k ( 5.37 ) for all Ŷ in Sŷ and for all Ŵ in Sŵ . Theorem 5.3 k Let y ' be a set of linearly contained patterns . Let S✩ be the reduced weight ...
... reduced training sequence Sy then creates a reduced weight - vector sequence Sŵ such that · k k ( 5.37 ) for all Ŷ in Sŷ and for all Ŵ in Sŵ . Theorem 5.3 k Let y ' be a set of linearly contained patterns . Let S✩ be the reduced weight ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 gi(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 TLU response 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 |