Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... normal or Gaussian probability - density function . is important because of its computational simplicity and because it represents a realistic model of many pattern - classification situations . Furthermore , normal distributions ...
... normal or Gaussian probability - density function . is important because of its computational simplicity and because it represents a realistic model of many pattern - classification situations . Furthermore , normal distributions ...
Sivu 54
... normal distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns ...
... normal distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns ...
Sivu 131
... normal patterns Consider the normal distribution expressed by where p ( X ) = 1 - - ( 2π ) d / 2 | | 31⁄2 exp { — 1⁄2 [ ( X — M ) ' Σ − 1 ( X — M ) ] } ( A.10 ) is the covariance matrix and M is the mean vector . From Eq . ( A - 6 ) we ...
... normal patterns Consider the normal distribution expressed by where p ( X ) = 1 - - ( 2π ) d / 2 | | 31⁄2 exp { — 1⁄2 [ ( X — M ) ' Σ − 1 ( X — M ) ] } ( A.10 ) is the covariance matrix and M is the mean vector . From Eq . ( A - 6 ) we ...
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 |