Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 32
... dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be ... linear dichotomies of N points dimensions, 1.
... dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be ... linear dichotomies of N points dimensions, 1.
Sivu 33
... linear dichotomies of X ' . We wish to find out by how much this number of linear dichotomies is increased if the set X ' is enlarged to 2 ძვ 15 le 4 X 2 2 -სვ 4 27 ( a ) Points in general position X2 16 -14 -15 ( b ) Three points ...
... linear dichotomies of X ' . We wish to find out by how much this number of linear dichotomies is increased if the set X ' is enlarged to 2 ძვ 15 le 4 X 2 2 -სვ 4 27 ( a ) Points in general position X2 16 -14 -15 ( b ) Three points ...
Sivu 67
... linear dichotomy of the N patterns , and , conversely , a dichotomy of the patterns in y is a linear dichotomy only ... dichotomies of N d - dimensional pat- terns implementable by a TLU , that is , the number of linear dichotomies of N ...
... linear dichotomy of the N patterns , and , conversely , a dichotomy of the patterns in y is a linear dichotomy only ... dichotomies of N d - dimensional pat- terns implementable by a TLU , that is , the number of linear dichotomies of N ...
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 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 |