Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 121
... Fix and Hodges method render it impractical in most pattern - classification tasks . To classify any pat- tern X , the distance between X and each of the patterns in the training subsets must be computed . If these computations are to ...
... Fix and Hodges method render it impractical in most pattern - classification tasks . To classify any pat- tern X , the distance between X and each of the patterns in the training subsets must be computed . If these computations are to ...
Sivu 122
... Fix and Hodges method . The concept of distance still plays an important role in a way which preserves some of the features of the Fix and Hodges method . It seems reasonable to assume that the k closest training patterns to a given ...
... Fix and Hodges method . The concept of distance still plays an important role in a way which preserves some of the features of the Fix and Hodges method . It seems reasonable to assume that the k closest training patterns to a given ...
Sivu 134
... Fix and Hodges method , 120 , 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density function ...
... Fix and Hodges method , 120 , 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density function ...
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 |