Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 82
Sivu 73
... mean that φ(α(w,λ)). Here, the problem is twofold. On the one hand, the researcher makes an assumption about the mean process. On the other hand, the researcher must decide whether to estimate all parameters with a uniform procedure ...
... mean that φ(α(w,λ)). Here, the problem is twofold. On the one hand, the researcher makes an assumption about the mean process. On the other hand, the researcher must decide whether to estimate all parameters with a uniform procedure ...
Sivu 487
... ( mean ) . By New Geneva Method St. Ber- and St. ( weighted nard Bernard mean ) . ( mean ) . ( weighted mean ) . Geneva . St. Ber- nard . Geneva and St. Bernard . ( 4 ) ( B ) ( 0 ) ( D ) ( A - D ) ( B - D ) ( 0 - D ) Meters . Meters ...
... ( mean ) . By New Geneva Method St. Ber- and St. ( weighted nard Bernard mean ) . ( mean ) . ( weighted mean ) . Geneva . St. Ber- nard . Geneva and St. Bernard . ( 4 ) ( B ) ( 0 ) ( D ) ( A - D ) ( B - D ) ( 0 - D ) Meters . Meters ...
Sivu 387
... Mean ! Rare have they long been among the people , who could practise it ! ' CHAP . IV . 1. The Master said , ' I know how it is that the path of the Mean is not walked in : -The knowing go beyond it , and the stupid do not come up to ...
... Mean ! Rare have they long been among the people , who could practise it ! ' CHAP . IV . 1. The Master said , ' I know how it is that the path of the Mean is not walked in : -The knowing go beyond it , and the stupid do not come up to ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components 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 step subsidiary discriminant Suppose terns 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 |