Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 120
... Fix and Hodges method.2 To determine the discriminant functions by the Fix and Hodges method , we first select some large positive integer k , which is small com- pared to the number of patterns in each of the training subsets . Next we ...
... Fix and Hodges method.2 To determine the discriminant functions by the Fix and Hodges method , we first select some large positive integer k , which is small com- pared to the number of patterns in each of the training subsets . Next we ...
Sivu 125
... Fix and Hodges method , presented in Sec . 7-4 , is the obvious R - category generalization of the two - category method whose properties were studied by Fix and Hodges.2 Achieving a reduction in storage re- quirements by ignoring all ...
... Fix and Hodges method , presented in Sec . 7-4 , is the obvious R - category generalization of the two - category method whose properties were studied by Fix and Hodges.2 Achieving a reduction in storage re- quirements by ignoring all ...
Sivu 134
... Fix and Hodges method , 120 , 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density function ...
... Fix and Hodges method , 120 , 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density function ...
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 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 |