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 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 ...
Sivu 120
... probability distributions are normal.3 There does exist a simple nonparametric rule , however , whose use implies only that the probability - density functions exist and that they are continuous . We shall call this rule the Fix and ...
... probability distributions are normal.3 There does exist a simple nonparametric rule , however , whose use implies only that the probability - density functions exist and that they are continuous . We shall call this rule the Fix and ...
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 |