Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 66
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights w1 , w2 , . . . , wa , Wa + 1 . Training a TLU to dichotomize ... Weight space,
... space into two half - spaces . One of these half- spaces is R1 ; the other is R2 . The hyperplane separating these half - spaces is determined by the TLU weights w1 , w2 , . . . , wa , Wa + 1 . Training a TLU to dichotomize ... Weight space,
Sivu 72
... weight space . The small arrows attached to these planes in this case indicate the side on which a TLU weight vector will give the desired response . The patterns will be presented cyclically in the following order : 1 , 2 , 3 , 4 , 1 ...
... weight space . The small arrows attached to these planes in this case indicate the side on which a TLU weight vector will give the desired response . The patterns will be presented cyclically in the following order : 1 , 2 , 3 , 4 , 1 ...
Sivu 85
... weight vectors satisfying inequality ( 5-6 ) . That is , W.Y > 0 for all W ... space representation , it is clear that the boundaries of W ' are ... weight vector in the reduced weight - vector sequence Sŵ . That is j | W - W1 | 2 = W. W ...
... weight vectors satisfying inequality ( 5-6 ) . That is , W.Y > 0 for all W ... space representation , it is clear that the boundaries of W ' are ... weight vector in the reduced weight - vector sequence Sŵ . That is j | W - W1 | 2 = W. W ...
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 |