Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 122
... mode . Thus the " closest - mode " method just de- scribed will often make decisions identical to those made by the Fix and Hodges method . What is needed to apply the closest - mode method is a means of training a PWL machine such that the ...
... mode . Thus the " closest - mode " method just de- scribed will often make decisions identical to those made by the Fix and Hodges method . What is needed to apply the closest - mode method is a means of training a PWL machine such that the ...
Sivu 123
... modes is generally much more difficult than that of estimating the mean of a distribution . The sample mean or ... mode ( uni- modal ) , then the center of gravity of a set of points is often a good esti- mate for this mode . In ...
... modes is generally much more difficult than that of estimating the mean of a distribution . The sample mean or ... mode ( uni- modal ) , then the center of gravity of a set of points is often a good esti- mate for this mode . In ...
Sivu 125
... modes was suggested by Sebestyen . 3,4 Closest - mode classification methods and mode - seeking training have been discussed by many workers . Sebestyen3.4 proposed a process that he called " Adaptive Sample Set Construction " to find ...
... modes was suggested by Sebestyen . 3,4 Closest - mode classification methods and mode - seeking training have been discussed by many workers . Sebestyen3.4 proposed a process that he called " Adaptive Sample Set Construction " to find ...
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 |