Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 52
... equal probability density ( 21220122122 +222 constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 012 When σ12 is zero , the contours ...
... equal probability density ( 21220122122 +222 constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line z1 = 22. The eccentricities of the ellipses are equal to 2012 VI + 012 When σ12 is zero , the contours ...
Sivu 58
... equal to rank QQ , which is equal to rank Qi , and since rank Q ; ≤ min ( d , N1 ) , rank ( Σ ) ; < d if N ; < d . If N ; ≥ d , Qi will have rank equal to d if and only if there are no linear dependencies among the rows of Q. Or ...
... equal to rank QQ , which is equal to rank Qi , and since rank Q ; ≤ min ( d , N1 ) , rank ( Σ ) ; < d if N ; < d . If N ; ≥ d , Qi will have rank equal to d if and only if there are no linear dependencies among the rows of Q. Or ...
Sivu 90
... equal to -Ŷk , and whose other components are all equal to zero . We apply this rule to each element of Sp to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the ...
... equal to -Ŷk , and whose other components are all equal to zero . We apply this rule to each element of Sp to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the ...
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 |