Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 16
Sivu 72
... weight vectors produced by the training procedure con- verges toward a solution weight vector . The fixed - increment and absolute correction rules are guaranteed to produce a solution weight vector after only a finite number of weight ...
... weight vectors produced by the training procedure con- verges toward a solution weight vector . The fixed - increment and absolute correction rules are guaranteed to produce a solution weight vector after only a finite number of weight ...
Sivu 75
... product of a weight vector with an augmented pattern vector ; that is , gi ( X ) = W ( i ) . Y for i = 1 , Ꭱ • ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine ...
... product of a weight vector with an augmented pattern vector ; that is , gi ( X ) = W ( i ) . Y for i = 1 , Ꭱ • ( 4.7 ) Simple extensions of the training procedures already discussed can be used to train a general linear machine ...
Sivu 88
... weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( k ) . Yk > W ( ) . Yk ... solution weight vectors . That is , for some set of indices k1 , k2 , KR , the set of vectors · " { W , ( k ) = W1 ...
... weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( k ) . Yk > W ( ) . Yk ... solution weight vectors . That is , for some set of indices k1 , k2 , KR , the set of vectors · " { W , ( k ) = W1 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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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 |