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 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 ...
... Point sets in E2 which map into category numbers 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 ...
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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |