Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 10
Sivu 88
... initial weight vectors , we can eliminate the occurrence of ( a ) by deleting from Sy all pattern vectors for which ( a ) occurs . The resulting sequence Sy will be called the reduced training se- quence . The resulting weight - vector ...
... initial weight vectors , we can eliminate the occurrence of ( a ) by deleting from Sy all pattern vectors for which ( a ) occurs . The resulting sequence Sy will be called the reduced training se- quence . The resulting weight - vector ...
Sivu 91
... weight vectors . As before , W is an open convex polyhedral cone . Let Sy , = Y ' , Y2 ' , Y1 ' , Y2 ' , . . . , Yx ... initial weight vector W1 we may remove from the training sequence those patterns Y ' for which W Yk ' > 0 . The ...
... weight vectors . As before , W is an open convex polyhedral cone . Let Sy , = Y ' , Y2 ' , Y1 ' , Y2 ' , . . . , Yx ... initial weight vector W1 we may remove from the training sequence those patterns Y ' for which W Yk ' > 0 . The ...
Sivu 123
... initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ( k + 1 ) st pattern in the training sequence is Xx + 1 , a member of category i . Which of the weight ...
... initial weight vectors be selected arbitrarily . * We shall describe the adjustments to be made at the kth step . Suppose that the ( k + 1 ) st pattern in the training sequence is Xx + 1 , a member of category i . Which of the weight ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |