Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 76
Sivu 69
... training patterns arẹ presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the TLU with the desired response dictated by the category of the pattern . The patterns may be tried ...
... training patterns arẹ presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the TLU with the desired response dictated by the category of the pattern . The patterns may be tried ...
Sivu 75
... training procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was ... training procedures already discussed can be used to train a general linear machine . Suppose we have a set y of ...
... training procedure for R > 2 A linear machine for classifying patterns belonging to more than two categories was ... training procedures already discussed can be used to train a general linear machine . Suppose we have a set y of ...
Sivu 121
... 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 be performed rapidly , each of the training patterns ...
... 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 be performed rapidly , each of the training patterns ...
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 |