Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... equal oppor- tunity to receive the same length of compulsory education. The ACOR's three alternative schemes reveal that the division of opinion on the pattern of second- ary education hinged upon the difference in various interest ...
... equal oppor- tunity to receive the same length of compulsory education. The ACOR's three alternative schemes reveal that the division of opinion on the pattern of second- ary education hinged upon the difference in various interest ...
Sivu 6804
... equal treatment of equals . The present system of public education .. in California fails to meet this criterion , both with respect to provision of services and with respect to the geographic distribution of the tax burden . ( Cal ...
... equal treatment of equals . The present system of public education .. in California fails to meet this criterion , both with respect to provision of services and with respect to the geographic distribution of the tax burden . ( Cal ...
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... equal civil rights to all American citizens. 9. Representative James Monroe, Ohio 3 Cong. Rec. 997 (1875): [The separate-but-equal provision is bad because it] introduces formally into the statute law a discrimination between different ...
... equal civil rights to all American citizens. 9. Representative James Monroe, Ohio 3 Cong. Rec. 997 (1875): [The separate-but-equal provision is bad because it] introduces formally into the statute law a discrimination between different ...
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 |