Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 36
Sivu 54
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. 3.7 The multivariate normal distribution The ... Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. 3.7 The multivariate normal distribution The ... Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or ...
Sivu 62
... normal distribution is well treated in a book by Anderson . " Equation ( 3 · 31 ) for the quadric discriminant functions , opti- mum for normal patterns , has been previously derived by Anderson and others . A similar derivation ...
... normal distribution is well treated in a book by Anderson . " Equation ( 3 · 31 ) for the quadric discriminant functions , opti- mum for normal patterns , has been previously derived by Anderson and others . A similar derivation ...
Sivu 136
... Normal patterns , 52 , 54 mean vector of , 53 , 54 transformation of , 131 Normal probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate , 54 Novikoff , 92 , 93 Null category , 3 Number of linear ...
... Normal patterns , 52 , 54 mean vector of , 53 , 54 transformation of , 131 Normal probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate , 54 Novikoff , 92 , 93 Null category , 3 Number of linear ...
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 |