Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 11
... apply to a large class of discriminant functions and are therefore of funda- mental importance . The concept of a layered machine is introduced in Chapter 6. Most of the pattern classifiers containing threshold elements that have been ...
... apply to a large class of discriminant functions and are therefore of funda- mental importance . The concept of a layered machine is introduced in Chapter 6. Most of the pattern classifiers containing threshold elements that have been ...
Sivu 90
... apply this rule to each element of Sy to generate the 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 ...
... apply this rule to each element of Sy to generate the 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 ...
Sivu 122
... apply the closest - mode method is a means of training a PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one , operating on a sequence ...
... apply the closest - mode method is a means of training a PWL machine such that the modes are identified and the appropriate discriminant functions are set up . This training process should be an iterative one , operating on a sequence ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 |