Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 24
Sivu 37
... Corresponding to each point X in the pattern space there is a point F { fi ( X ) , fм ( X ) ) in space ; therefore , corresponding to the set X of N points in general position in the pattern space , there is a set F of N points in ...
... Corresponding to each point X in the pattern space there is a point F { fi ( X ) , fм ( X ) ) in space ; therefore , corresponding to the set X of N points in general position in the pattern space , there is a set F of N points in ...
Sivu 67
... Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional , augmented patterns Y1 and Y2 . Each element of Y1 and Y2 is obtained by augmenting the patterns in X1 and X2 , respectively . We shall denote the ...
... Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional , augmented patterns Y1 and Y2 . Each element of Y1 and Y2 is obtained by augmenting the patterns in X1 and X2 , respectively . We shall denote the ...
Sivu 112
... ( corresponding to category 1 and category 2 ) depending on the nature of the switching function H ( U ) implemented by the subsequent layers . It must be pointed out , however , that these 2o subsidiary discriminant func- tions are not ...
... ( corresponding to category 1 and category 2 ) depending on the nature of the switching function H ( U ) implemented by the subsequent layers . It must be pointed out , however , that these 2o subsidiary discriminant func- tions are not ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
LAYERED MACHINES | 95 |
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 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 vector 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 |