Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 37
... pattern space satisfying the equations g ( X ) = 0 are families of surfaces . The separations that these surfaces ( called surfaces ) effect on a set X of N points are called dichotomies . If there is no surface in the pattern space ...
... pattern space satisfying the equations g ( X ) = 0 are families of surfaces . The separations that these surfaces ( called surfaces ) effect on a set X of N points are called dichotomies . If there is no surface in the pattern space ...
Sivu 66
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We recall that a TLU implements a hyperplane decision surface which divides the pattern space into two half - spaces . One of these half- spaces is R1 ; the other is ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. We recall that a TLU implements a hyperplane decision surface which divides the pattern space into two half - spaces . One of these half- spaces is R1 ; the other is ...
Sivu 105
... Pattern space 3 x2 1,4,5,8 TLU 3 Origin TLU 2 - * 3,7 TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from the pattern ...
... Pattern space 3 x2 1,4,5,8 TLU 3 Origin TLU 2 - * 3,7 TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from the pattern ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 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 |