Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 36
Sivu 28
... called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision ...
... called positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision ...
Sivu 54
... called the mean vector . ma Old σ11 012 σij Odd σdi is a symmetric , positive definite matrix , called the covariance matrix . The i , j component o1 ; of the covariance matrix Σ is given by - σij = E [ ( xim ; ) ( x ; — m ; ) ] ( 3.25 ) ...
... called the mean vector . ma Old σ11 012 σij Odd σdi is a symmetric , positive definite matrix , called the covariance matrix . The i , j component o1 ; of the covariance matrix Σ is given by - σij = E [ ( xim ; ) ( x ; — m ; ) ] ( 3.25 ) ...
Sivu 69
... called the correction increment . It controls the extent of the adjustment . For sufficiently large c , the weight point will cross the pattern hyperplane , and Y. W ' will be correctly positive . If W were incorrectly on the positive ...
... called the correction increment . It controls the extent of the adjustment . For sufficiently large c , the weight point will cross the pattern hyperplane , and Y. W ' will be correctly positive . If W were incorrectly on the positive ...
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 |