Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 3
Sivu 80
... Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , ... 1. Each Y in Sy is a member of y . k Yk . 2. Every element of y occurs infinitely often in Sy . ( 5.2 ) The training problem for a two - category linear machine ...
... Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , ... 1. Each Y in Sy is a member of y . k Yk . 2. Every element of y occurs infinitely often in Sy . ( 5.2 ) The training problem for a two - category linear machine ...
Sivu 88
... Sy all pattern vectors for which ( a ) occurs . The resulting sequence Sy will be called the reduced training se- quence . The resulting weight - vector sequences Sŵ ,, Sŵ , . , SŵR gener- ated from Sŷ by the above rule will be called ...
... Sy all pattern vectors for which ( a ) occurs . The resulting sequence Sy will be called the reduced training se- quence . The resulting weight - vector sequences Sŵ ,, Sŵ , . , SŵR gener- ated from Sŷ by the above rule will be called ...
Sivu 89
... 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 , Y of Sy is a vector Z ...
... 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 , Y of Sy is a vector Z ...
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 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 |