Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 27
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 91 ( 1 ) and 92 ( 1 ) linearly separable . For any given training subsets X1 and X2 it would be of ...
... fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves 91 ( 1 ) and 92 ( 1 ) linearly separable . For any given training subsets X1 and X2 it would be of ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |