Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 66
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights w1 , w2 , , wa , wa + 1 . Training a TLU to dichotomize correctly the ...
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights w1 , w2 , , wa , wa + 1 . Training a TLU to dichotomize correctly the ...
Sivu 104
... space is trans- formed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call the first image space or the I1 space . The trans- formation between the pattern space and the I space depends on the values ...
... space is trans- formed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call the first image space or the I1 space . The trans- formation between the pattern space and the I space depends on the values ...
Sivu 105
... space cube in accordance with the TLU Origin 6 8 TLU 1 ( a ) Pattern space 3 x2 1,4,5,8 TLU 3 Origin TLU 2 * 3,7 TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an ...
... space cube in accordance with the TLU Origin 6 8 TLU 1 ( a ) Pattern space 3 x2 1,4,5,8 TLU 3 Origin TLU 2 * 3,7 TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an ...
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 |