Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 49
... probability values . These optimum estimates are meaningful , however , only when the unknown proba- bility values are themselves random variables with known probability distributions . As an example , consider the case of N successive ...
... probability values . These optimum estimates are meaningful , however , only when the unknown proba- bility values are themselves random variables with known probability distributions . As an example , consider the case of N successive ...
Sivu 50
... distributions encompass some situations in which the individual pattern components are not statistically independent . In the following sections we will review briefly some of the properties of the ... probability-density function,
... distributions encompass some situations in which the individual pattern components are not statistically independent . In the following sections we will review briefly some of the properties of the ... probability-density function,
Sivu 118
... probability distributions . This advantage is especially im- portant in multimodal pattern - classifying tasks . The nonparametric rules that we have discussed so far have all been error - correction rules , and it must be said that ...
... probability distributions . This advantage is especially im- portant in multimodal pattern - classifying tasks . The nonparametric rules that we have discussed so far have all been error - correction rules , and it must be said that ...
Sisältö
Preface vii | 11 |
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 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 |