Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 117
... bank . Thus , training also in- volves shuffling the subsidiary discriminators from bank to bank until some appropriate organization is found . * What is needed , then , to train PWL machines is a method of adjust- ing weight vectors ...
... bank . Thus , training also in- volves shuffling the subsidiary discriminators from bank to bank until some appropriate organization is found . * What is needed , then , to train PWL machines is a method of adjust- ing weight vectors ...
Sivu 118
... bank , j # i , contained the largest subsidiary discriminant . The adjustment method first subtracts Y from the weight vector used by this subsidiary discriminant function in the jth bank . Of those subsidiary discriminant functions in ...
... bank , j # i , contained the largest subsidiary discriminant . The adjustment method first subtracts Y from the weight vector used by this subsidiary discriminant function in the jth bank . Of those subsidiary discriminant functions in ...
Sivu 122
... banks of weight vectors , and the ith bank has L members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category patterns will be ...
... banks of weight vectors , and the ith bank has L members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category patterns will be ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
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 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 theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |