Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 47
Sivu 102
... pattern vector . Consider the three pattern vectors and their corresponding pattern hyperplanes ( lines ) shown in Fig . 6.5 . The arrows indicate the positive sides of the lines . In this figure is shown the history of weight - vector ...
... pattern vector . Consider the three pattern vectors and their corresponding pattern hyperplanes ( lines ) shown in Fig . 6.5 . The arrows indicate the positive sides of the lines . In this figure is shown the history of weight - vector ...
Sivu 103
... vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) ... vector . If there are P1TLUS in the first layer , these TLUs transform the d - dimen- sional input pattern vector into ...
... vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) ... vector . If there are P1TLUS in the first layer , these TLUs transform the d - dimen- sional input pattern vector into ...
Sivu 136
... patterns , 52 , 54 mean vector of , 53 , 54 transformation of , 131 Normal probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate , 54 Novikoff , 92 , 93 Null category , 3 Number of linear ...
... patterns , 52 , 54 mean vector of , 53 , 54 transformation of , 131 Normal probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate , 54 Novikoff , 92 , 93 Null category , 3 Number of linear ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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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 |