Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 34
Sivu 29
... components f1 , f2 , fм are functions of the xi , i = " • d . The first d components of F are x12 , x22 , x2 ; the next d ( d1 ) / 2 components are all the pairs x1x2 , x1x3 , 1 , · xa - 1a ; the last d components are x1 , x2 , · 9 xa ...
... components f1 , f2 , fм are functions of the xi , i = " • d . The first d components of F are x12 , x22 , x2 ; the next d ( d1 ) / 2 components are all the pairs x1x2 , x1x3 , 1 , · xa - 1a ; the last d components are x1 , x2 , · 9 xa ...
Sivu 50
... components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the discriminant function for the optimum classifying machine . The optimum classifier can also be ...
... components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the discriminant function for the optimum classifying machine . The optimum classifier can also be ...
Sivu 111
... components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ... components , and G2 ( X ) is a vector with 2P - L components . Let the ith component of G1 ( X ) be denoted by ...
... components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ... components , and G2 ( X ) is a vector with 2P - L components . Let the ith component of G1 ( X ) be denoted by ...
Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix 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 Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |