Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 98
... committee ” of weight vectors W1 , W2 , and W , in Fig . 6.3 . With respect to these weight vectors , we have the inequalities 18 1 W2 . Y1 < 0 W1 . Y1 > 0 1 W1 . Y2 > 0 2 1 W1 . Y3 > 0 C • W2 Y2 > 0 W2 . Y3 < 0 ( 6.4 ) W3 . Y1 > 0 W3 ...
... committee ” of weight vectors W1 , W2 , and W , in Fig . 6.3 . With respect to these weight vectors , we have the inequalities 18 1 W2 . Y1 < 0 W1 . Y1 > 0 1 W1 . Y2 > 0 2 1 W1 . Y3 > 0 C • W2 Y2 > 0 W2 . Y3 < 0 ( 6.4 ) W3 . Y1 > 0 W3 ...
Sivu 99
... committee TLUS ( first layer ) Response Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the training ...
... committee TLUS ( first layer ) Response Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the training ...
Sivu 100
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Yk . For ex- ample , if exactly seven TLUs in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven ...
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Yk . For ex- ample , if exactly seven TLUs in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven ...
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 |