Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 49
... 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 Bernoulli ...
... 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 Bernoulli ...
Sivu 50
... values of the TLU weights and threshold . 3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an ...
... values of the TLU weights and threshold . 3.6 The bivariate normal probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an ...
Sivu 52
... variables 1 and 2 is more complicated * than that of Eq . ( 3 · 18 ) , but the general properties of the function ... random variables . The assumption made in the previous footnote should now be generalized to σ122 < σ11022 . center of ...
... variables 1 and 2 is more complicated * than that of Eq . ( 3 · 18 ) , but the general properties of the function ... random variables . The assumption made in the previous footnote should now be generalized to σ122 < σ11022 . center of ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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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 |