Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 70
... correction increment , which will be discussed more fully below . There are several types of error - correction procedures . We shall men- tion three of them here . These differ solely in the interpretation to be given to the value of ...
... correction increment , which will be discussed more fully below . There are several types of error - correction procedures . We shall men- tion three of them here . These differ solely in the interpretation to be given to the value of ...
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 W 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 W is ...
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 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |