Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 51
Sivu 5
... Point sets in E2 which map into category numbers 9 points in Ed which are mapped into the number i . Then , for each category number , we have a set of points in Ed denoted by one of the symbols R1 , R2 , RR . As an example , consider ...
... Point sets in E2 which map into category numbers 9 points in Ed which are mapped into the number i . Then , for each category number , we have a set of points in Ed denoted by one of the symbols R1 , R2 , RR . As an example , consider ...
Sivu 32
... points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will depend only on the number of patterns N and the number of parameters M + 1 of the function , not on the configuration of ...
... points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will depend only on the number of patterns N and the number of parameters M + 1 of the function , not on the configuration of ...
Sivu 36
... points and a set Z of K points ( K < d ) in Ed . We desire to know the number Lz ( N , d ) of linear dichot- omies of X achievable by a hyperplane constrained to contain all the points of Z. We shall assume that the points of Z are in ...
... points and a set Z of K points ( K < d ) in Ed . We desire to know the number Lz ( N , d ) of linear dichot- omies of X achievable by a hyperplane constrained to contain all the points of Z. We shall assume that the points of Z are in ...
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 |