Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 24
Sivu 28
... 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 surfaces The ...
... 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 surfaces The ...
Sivu 54
... positive definite matrix , called the covariance matrix . The i , j component σ ;; of the covariance matrix Σ is given by for all i , j = 1 , write σij = E [ ( xi — mi ) ( x ; — m ; ) ] ( 3.25 ) , d ; in particular , σ is the variance ...
... positive definite matrix , called the covariance matrix . The i , j component σ ;; of the covariance matrix Σ is given by for all i , j = 1 , write σij = E [ ( xi — mi ) ( x ; — m ; ) ] ( 3.25 ) , d ; in particular , σ is the variance ...
Sivu 100
... positive dot product . That is , a weight vector lying on a pattern hyperplane is assumed to be on the positive side . Other conventions could also have been adopted . weight vectors which are adjusted are those which have dot 100 ...
... positive dot product . That is , a weight vector lying on a pattern hyperplane is assumed to be on the positive side . Other conventions could also have been adopted . weight vectors which are adjusted are those which have dot 100 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |