Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 22
Sivu 103
... adjusted , and W1 and W. would wander around perpetually in a futile search for stable locations , which do not exist so long as W2 cannot cooperate by leaving its initial region . * This same phenomenon accounts for instances in which ...
... adjusted , and W1 and W. would wander around perpetually in a futile search for stable locations , which do not exist so long as W2 cannot cooperate by leaving its initial region . * This same phenomenon accounts for instances in which ...
Sivu 116
... adjusted in some appropri- ate manner . Such adjustments are effected by changing the weight vector associated with each of the subsidiary discriminators . If the PWL ma- chine has a total of L subsidiary discriminators , there will be ...
... adjusted in some appropri- ate manner . Such adjustments are effected by changing the weight vector associated with each of the subsidiary discriminators . If the PWL ma- chine has a total of L subsidiary discriminators , there will be ...
Sivu 123
... adjustments to the ( d + 1 ) st components . Suppose that the jth weight vector in this bank is the closest one to X + 1 . Then , only this closest weight vector is adjusted and all other weight vectors ( including all those in the ...
... adjustments to the ( d + 1 ) st components . Suppose that the jth weight vector in this bank is the closest one to X + 1 . Then , only this closest weight vector is adjusted and all other weight vectors ( including all those in the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |