Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 80
... correction increment c is a positive number , possibly depend- ing on k . That is , the ( k + 1 ) st weight vector ... error - correction procedure . If Ckc > 0 is a constant not dependent on k , then the rule is a fixed - increment ...
... correction increment c is a positive number , possibly depend- ing on k . That is , the ( k + 1 ) st weight vector ... error - correction procedure . If Ckc > 0 is a constant not dependent on k , then the rule is a fixed - increment ...
Sivu 81
... fixed - increment error - correction procedure and beginning with any initial weight vector W1 . Then , for some finite index ko , 1 . W ko = Wko + 1 = W ko + 2 == is a solution vector . Discussion The value of the fixed correction ...
... fixed - increment error - correction procedure and beginning with any initial weight vector W1 . Then , for some finite index ko , 1 . W ko = Wko + 1 = W ko + 2 == is a solution vector . Discussion The value of the fixed correction ...
Sivu 133
... correction rule , 70 , 81 ADALINES , 77 Adaptive decision networks , 2 Adaptive sample set construction , 125 ... error - correction train- ing methods , 71 , 72 , 75 of fixed - increment rule , 82 , 85 of fractional correction rule , 91 ...
... correction rule , 70 , 81 ADALINES , 77 Adaptive decision networks , 2 Adaptive sample set construction , 125 ... error - correction train- ing methods , 71 , 72 , 75 of fixed - increment rule , 82 , 85 of fractional correction rule , 91 ...
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 |