Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 80
... correction increment c is a positive number , possibly depend- ing on k . That is , the ( k + 1 ) st weight vector depends only on the kth pattern , the correction increment , and the previous weight vector . The weight vector WA is ...
... correction increment c is a positive number , possibly depend- ing on k . That is , the ( k + 1 ) st weight vector depends only on the kth pattern , the correction increment , and the previous weight vector . The weight vector WA is ...
Sivu 81
... correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of cÅ is taken to be the smallest integer for which cÂY · Y > │W✩ • Yk❘ . With this pro ...
... correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of cÅ is taken to be the smallest integer for which cÂY · Y > │W✩ • Yk❘ . With this pro ...
Sivu 133
... correction train- ing methods , 71 , 72 , 75 of fixed - increment rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions ...
... correction train- ing methods , 71 , 72 , 75 of fixed - increment rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |