Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 80
... initial weight vector W1 is arbitrary . We shall be interested here in sequences Sw , which are recursively generated from a training sequence Sy by the following rules : W ko = k 1. If the kth member of the training sequence Y is ...
... initial weight vector W1 is arbitrary . We shall be interested here in sequences Sw , which are recursively generated from a training sequence Sy by the following rules : W ko = k 1. If the kth member of the training sequence Y is ...
Sivu 88
... initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( ) . Yk > W , ( k ) . Yk j = 1 , • 9 R , ji or ( b ) there exists some l = 1 , R , li for which · W ( ) YW , ( k ) • Yk j = 1 , " R ...
... initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( ) . Yk > W , ( k ) . Yk j = 1 , • 9 R , ji or ( b ) there exists some l = 1 , R , li for which · W ( ) YW , ( k ) • Yk j = 1 , " R ...
Sivu 103
... initial region . * This same phenomenon accounts for instances in which the committee train- ing procedure does not converge even when the initial weight vectors are all of the same length . Occasionally one of the weight vectors ...
... initial region . * This same phenomenon accounts for instances in which the committee train- ing procedure does not converge even when the initial weight vectors are all of the same length . Occasionally one of the weight vectors ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 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 |