Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 54
... 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 ) for all i , j = 1 , . . . , d ; in particular , σ is the variance of x . We can also ...
... 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 ) for all i , j = 1 , . . . , d ; in particular , σ is the variance of x . We can also ...
Sivu 58
... covariance matrix . The first step in its de- velopment is to form a matrix Q ; whose columns are derived from the patterns in X. Subtract from each of the N ; patterns in X ; the sample- mean pattern ( X ) ;; Q ; is then a d X N ; matrix ...
... covariance matrix . The first step in its de- velopment is to form a matrix Q ; whose columns are derived from the patterns in X. Subtract from each of the N ; patterns in X ; the sample- mean pattern ( X ) ;; Q ; is then a d X N ; matrix ...
Sivu 59
... covariance matrices are all known but for which the mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d ...
... covariance matrices are all known but for which the mean vectors are assumed to be random variables . Suppose the pattern vectors belonging to category i are normal with known covariance matrix Σ ; and unknown mean vector . Thus , the d ...
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 |