Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 76
Sivu 69
... training patterns are presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the TLU with the desired response dictated by the category of the pattern . The patterns may be tried ...
... training patterns are presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the TLU with the desired response dictated by the category of the pattern . The patterns may be tried ...
Sivu 75
... train a general linear machine . Suppose we have a set y of augmented training patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset y ; con- tains all training patterns in y belonging to category i ...
... train a general linear machine . Suppose we have a set y of augmented training patterns divided into subsets Y1 , 2 , . . . , YR which are linearly separable . The subset y ; con- tains all training patterns in y belonging to category i ...
Sivu 122
... training patterns to a given pattern X will often include a predominant number of patterns from the cluster sur- rounding the closest mode . Thus the " closest - mode " method just de- scribed will often make decisions identical to ...
... training patterns to a given pattern X will often include a predominant number of patterns from the cluster sur- rounding the closest mode . Thus the " closest - mode " method just de- scribed will often make decisions identical to ...
Sisältö
Preface vii | 11 |
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 important 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |