Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 100
... dot products with Y. Let the weight vec- tors at this stage be given by W1 ) , W2 * ) , and Wp ( ) . 2 · " In ... dot product with Y minus the number having a negative dot product with Y. * Thus , N is the sum operated on by the ...
... dot products with Y. Let the weight vec- tors at this stage be given by W1 ) , W2 * ) , and Wp ( ) . 2 · " In ... dot product with Y minus the number having a negative dot product with Y. * Thus , N is the sum operated on by the ...
Sivu 103
... dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) are on the correct side ; thus , no adjustments are ...
... dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) are on the correct side ; thus , no adjustments are ...
Sivu 110
... dot products Y. W1 , Y. W2 , Y. Wp by the symbols fi ( Y ) , ƒ2 ( Y ) , . . , fp ( Y ) , respectively . i . · · Now , still considering the first layer of TLUS , let us look ahead through the remaining layers toward the final response ...
... dot products Y. W1 , Y. W2 , Y. Wp by the symbols fi ( Y ) , ƒ2 ( Y ) , . . , fp ( Y ) , respectively . i . · · Now , still considering the first layer of TLUS , let us look ahead through the remaining layers toward the final response ...
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 |