Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 10
Sivu 33
... derivations to follow in this and subsequent sections , we shall use some facts from geometry which , while obvious for two- and three - dimensional spaces , happen to be valid in any finite - dimensional space . Of course , each of ...
... derivations to follow in this and subsequent sections , we shall use some facts from geometry which , while obvious for two- and three - dimensional spaces , happen to be valid in any finite - dimensional space . Of course , each of ...
Sivu 62
... derivation is given by Minsky . Winder1 has determined that the weights specified by Eqs . ( 3 · 14 ) and ( 3 · 15 ) of this example will realize only a small percentage of the linearly separable switching functions and suggests another ...
... derivation is given by Minsky . Winder1 has determined that the weights specified by Eqs . ( 3 · 14 ) and ( 3 · 15 ) of this example will realize only a small percentage of the linearly separable switching functions and suggests another ...
Sivu 77
... derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron . 12 The error - correction training procedures discussed in Sec . 4.3 stem from a variety of sources . The fixed - increment and absolute ...
... derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron . 12 The error - correction training procedures discussed in Sec . 4.3 stem from a variety of sources . The fixed - increment and absolute ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(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 |