Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 70
... fixed - increment rule , c is taken to be any fixed number greater than zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the corresponding pattern component . This adjustment may ...
... fixed - increment rule , c is taken to be any fixed number greater than zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the corresponding pattern component . This adjustment may ...
Sivu 71
... fixed constant so that the distance moved toward a particular pattern hyperplane is always the same . This fixed distance . may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is ...
... fixed constant so that the distance moved toward a particular pattern hyperplane is always the same . This fixed distance . may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is ...
Sivu 107
... fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves ( 1 ) and 2 ( 1 ) linearly separable . For any given training subsets X1 and X2 it would be of interest ...
... fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves ( 1 ) and 2 ( 1 ) linearly separable . For any given training subsets X1 and X2 it would be of interest ...
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 |