Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 8
Sivu 104
... image space or the I1 space . The trans- formation between the pattern space and the I space depends on the values of the weights in the first layer . For a given set of weights , the first layer will transform a finite set X of pattern ...
... image space or the I1 space . The trans- formation between the pattern space and the I space depends on the values of the weights in the first layer . For a given set of weights , the first layer will transform a finite set X of pattern ...
Sivu 105
... image - space cube in accordance with the TLU 5 6 TLU 3 8 6 TLU 1 TLU 2 Origin * 1 ( a ) Pattern space 1,4,5,8 TLU 3 Origin TLU 2 * 3,7 TLU 1 ( b ) Image space a FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 ...
... image - space cube in accordance with the TLU 5 6 TLU 3 8 6 TLU 1 TLU 2 Origin * 1 ( a ) Pattern space 1,4,5,8 TLU 3 Origin TLU 2 * 3,7 TLU 1 ( b ) Image space a FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 ...
Sivu 107
... space , and this hyper- plane is the perpendicular bisector of a major diagonal of the image - space cube . The process of training the committee machine is then a search for a pattern - space to image - space transformation such that ...
... space , and this hyper- plane is the perpendicular bisector of a major diagonal of the image - space cube . The process of training the committee machine is then a search for a pattern - space to image - space transformation such that ...
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 |