Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 80
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A training sequence on y , denoted by Sy , is any infinite sequence of patterns such that = Sy Y1 , Y2 , ... , Yx , ... k , 1. Each Y in Sy is a member of Y. k 2 ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. A training sequence on y , denoted by Sy , is any infinite sequence of patterns such that = Sy Y1 , Y2 , ... , Yx , ... k , 1. Each Y in Sy is a member of Y. k 2 ...
Sivu 89
... training set y . Each vector Z in Z is of RD dimensions ; it will be ... training subsets ; suppose it belongs -- 2. We denote each of the R 1 vectors in Z ... sequence Sy and the reduced weight - vector sequences Sŵ ,, ... , SŴR a ...
... training set y . Each vector Z in Z is of RD dimensions ; it will be ... training subsets ; suppose it belongs -- 2. We denote each of the R 1 vectors in Z ... sequence Sy and the reduced weight - vector sequences Sŵ ,, ... , SŴR a ...
Sivu 93
... training sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that mem- ber of y ' which has the smallest ( most - negative ) dot product with Wk + 1 . They ...
... training sequence which is generated recursively from the weight - vector sequence . The ( k + 1 ) th pattern in the training sequence is that mem- ber of y ' which has the smallest ( most - negative ) dot product with Wk + 1 . They ...
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 |