Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 110
... binary output of the ith TLU in the first layer be denoted by u , and let the weight vector corresponding to this TLU be denoted by W ;. For any given augmented input pattern Y each Ui = +1 or 1 , depending on whether Y W , is greater ...
... binary output of the ith TLU in the first layer be denoted by u , and let the weight vector corresponding to this TLU be denoted by W ;. For any given augmented input pattern Y each Ui = +1 or 1 , depending on whether Y W , is greater ...
Sivu 111
... binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let H1 be a matrix whose rows consist of those U vectors for which H ( U ) +1 . Let ...
... binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let H1 be a matrix whose rows consist of those U vectors for which H ( U ) +1 . Let ...
Sivu 136
... binary pat- terns , 48 for normal patterns , 55 Optimum machines , 44 Overlapping probability distributions , 118 Overrelaxation , 77 Pairwise linearly separable subsets , 21 Parameters , 9 , 15 , 43 , 44 Parametric training , 9 , 10 ...
... binary pat- terns , 48 for normal patterns , 55 Optimum machines , 44 Overlapping probability distributions , 118 Overrelaxation , 77 Pairwise linearly separable subsets , 21 Parameters , 9 , 15 , 43 , 44 Parametric training , 9 , 10 ...
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 |