Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 9
Sivu 80
... initial weight vector W1 is arbitrary . We shall be interested here in sequences Sw , which are recursively generated from a training sequence Sy by the following rules : W1 , W2 , W3 W ko + 2 ko + 1 = = k 1. If the kth member of the ...
... initial weight vector W1 is arbitrary . We shall be interested here in sequences Sw , which are recursively generated from a training sequence Sy by the following rules : W1 , W2 , W3 W ko + 2 ko + 1 = = k 1. If the kth member of the ...
Sivu 88
... initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . or Then , either ( a ) W , ( k ) . Yk > W1 ( k ) . Yk j = 1 , · R , ji = 1 , R , li for which W1 ( k ) • Yk > Wj ( k ) • Yk j = 1 , " R , j #l ( b ) there ...
... initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . or Then , either ( a ) W , ( k ) . Yk > W1 ( k ) . Yk j = 1 , · R , ji = 1 , R , li for which W1 ( k ) • Yk > Wj ( k ) • Yk j = 1 , " R , j #l ( b ) there ...
Sivu 103
... initial region . * This same phenomenon accounts for instances in which the committee train- ing procedure does not converge even when the initial weight vectors are all of the same length . Occasionally one of the weight vectors ...
... initial region . * This same phenomenon accounts for instances in which the committee train- ing procedure does not converge even when the initial weight vectors are all of the same length . Occasionally one of the weight vectors ...
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 |