Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 101
... adjusted are those which have dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ( or subtracting ) the ...
... adjusted are those which have dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ( or subtracting ) the ...
Sivu 102
... all of them ( hence , the majority ) are on the incorrect side . This situation calls for the adjustment of two of them , and W1 ( 1 ) and W2 ( 1 ) are adjusted as shown since they are the closest to 102 LAYERED MACHINES.
... all of them ( hence , the majority ) are on the incorrect side . This situation calls for the adjustment of two of them , and W1 ( 1 ) and W2 ( 1 ) are adjusted as shown since they are the closest to 102 LAYERED MACHINES.
Sivu 103
... adjusted , and W1 and W3 would wander around perpetually in a futile search for stable locations , which do not exist so long as W2 cannot cooperate by leaving its initial region . * This same phenomenon accounts for instances in which ...
... adjusted , and W1 and W3 would wander around perpetually in a futile search for stable locations , which do not exist so long as W2 cannot cooperate by leaving its initial region . * This same phenomenon accounts for instances in which ...
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 |