Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 88
... sequences generated by any training sequence Sy , using the generalized fixed - increment error - correc- tion procedure and beginning with any initial weight vectors . Then , the weight - vector sequences will eventually produce a set ...
... sequences generated by any training sequence Sy , using the generalized fixed - increment error - correc- tion procedure and beginning with any initial weight vectors . Then , the weight - vector sequences will eventually produce a set ...
Sivu 89
... training set y . Each vector Z in Z is of RD dimensions ; it will be ... training subsets ; suppose it belongs - 2. We denote each of the R 1 vectors in Z generated by ... sequence Sy and the reduced weight - vector sequences Sŵ ,, SŴR a ...
... training set y . Each vector Z in Z is of RD dimensions ; it will be ... training subsets ; suppose it belongs - 2. We denote each of the R 1 vectors in Z generated by ... sequence Sy and the reduced weight - vector sequences Sŵ ,, SŴR a ...
Sivu 90
... sequence Sz . 9 The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, .. SŵR . Let V be the kth member of the sequence Sv . If the respective kth mem ...
... sequence Sz . 9 The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, .. SŵR . Let V be the kth member of the sequence Sv . If the respective kth mem ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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Yleiset termit ja lausekkeet
assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS 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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 |