Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 33
Sivu 32
... number of 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 ... number of linear dichotomies of N points dimensions, 1.
... number of 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 ... number of linear dichotomies of N points dimensions, 1.
Sivu 34
... number of linear dichotomies of the complete set X is given by L ( N , d ) - - - = L ( N − 1 , d ) — Lx , ( N − 1 , d ) + 2Lx , ( N − 1 , d ) L ( N1 , d ) + Lx ( N − 1 , d ) = N where L ( N- 1 , d ) = - ( 2 · 31 ) - 1 points the ...
... number of linear dichotomies of the complete set X is given by L ( N , d ) - - - = L ( N − 1 , d ) — Lx , ( N − 1 , d ) + 2Lx , ( N − 1 , d ) L ( N1 , d ) + Lx ( N − 1 , d ) = N where L ( N- 1 , d ) = - ( 2 · 31 ) - 1 points the ...
Sivu 41
... number of linear dichotomies and the extension of these results to surfaces are also due to Cover.7 Based on experimental and theoretical results on the number of linear dichotomies , both Koford12 and Brown13 suggested that the ...
... number of linear dichotomies and the extension of these results to surfaces are also due to Cover.7 Based on experimental and theoretical results on the number of linear dichotomies , both Koford12 and Brown13 suggested that the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |