Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 25
Sivu 30
... function can be con- sidered to be a linear function of the components of a ... ( X ; W1 , W2 , , wм + 1 ) which depends linearly on the parame- ters . A ... g . ( X ) and surface in the pattern space has corresponding space ...
... function can be con- sidered to be a linear function of the components of a ... ( X ; W1 , W2 , , wм + 1 ) which depends linearly on the parame- ters . A ... g . ( X ) and surface in the pattern space has corresponding space ...
Sivu 47
... ( X ) = log p ( xi ) + log p ( i ) for i = 1 , which leads to the same decisions since the log function is a monotonic ... g ( X ) = 91 ( X ) — 92 ( X ) . If g ( X ) > 0 , the machine places X in category 1 ; if g ( X ) < 0 , the ...
... ( X ) = log p ( xi ) + log p ( i ) for i = 1 , which leads to the same decisions since the log function is a monotonic ... g ( X ) = 91 ( X ) — 92 ( X ) . If g ( X ) > 0 , the machine places X in category 1 ; if g ( X ) < 0 , the ...
Sivu 111
... function H ( U ) . To find the discriminant function of a layered machine , given a set of P functions fi ( Y ) , i = 1 , . . . , P and a switching function H ( U ) , we must find a g ( X ) such that +1 = 1 g ( x ) > 0 g ( X ) < 0 when X ...
... function H ( U ) . To find the discriminant function of a layered machine , given a set of P functions fi ( Y ) , i = 1 , . . . , P and a switching function H ( U ) , we must find a g ( X ) such that +1 = 1 g ( x ) > 0 g ( X ) < 0 when X ...
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 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 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 subsets X1 subsidiary discriminant functions Suppose terns 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 |