Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 75
... correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two ... error - correction procedure can be used to train the general linear machine and thus find ( solution ) weight vectors when ...
... correction training procedure for R > 2 A linear machine for classifying patterns belonging to more than two ... error - correction procedure can be used to train the general linear machine and thus find ( solution ) weight vectors when ...
Sivu 118
... procedure is clear ; it is an attempt to apply something like the generalized error- correction procedure to a structure containing more than one subsidiary discriminant function per bank . The conditions ( if any ) under which this ...
... procedure is clear ; it is an attempt to apply something like the generalized error- correction procedure to a structure containing more than one subsidiary discriminant function per bank . The conditions ( if any ) under which this ...
Sivu 119
... error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors ...
... error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the optimum surface since inevitable errors ...
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 |