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 123
... bank is the closest to X + 1 can now be determined , using the PWL machine itself if Eq . ( 7 · 2 ) is used to provide continuous adjustments to the ( d + 1 ) st components . Suppose that the jth weight vector in this bank is the ...
... bank is the closest to X + 1 can now be determined , using the PWL machine itself if Eq . ( 7 · 2 ) is used to provide continuous adjustments to the ( d + 1 ) st components . Suppose that the jth weight vector in this bank is the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |