Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 5
Sivu 98
... majority of the weight vectors yield dot products having the correct sign . Therefore these three weight vectors can be employed by a committee of three TLUS in such a Y1 W , 2 1 Y3 W2 FIGURE 6.3 A commmittee of weight vectors way that ...
... majority of the weight vectors yield dot products having the correct sign . Therefore these three weight vectors can be employed by a committee of three TLUS in such a Y1 W , 2 1 Y3 W2 FIGURE 6.3 A commmittee of weight vectors way that ...
Sivu 100
... majority of the weight vectors have negative dot products with Y. Let the weight vec- tors at this stage be given by W1 ) , W2 * ) , and Wp ( ) . 2 · " In describing the rule for modifying the weight vectors we shall make use of the ...
... majority of the weight vectors have negative dot products with Y. Let the weight vec- tors at this stage be given by W1 ) , W2 * ) , and Wp ( ) . 2 · " In describing the rule for modifying the weight vectors we shall make use of the ...
Sivu 110
... majority function ( i.e. , H ( U ) = +1 if the majority of the u ; = +1 ) , then the layered machine is a committee machine . The effect of training any of the TLUS in the first layer of a layered machine ( i.e. , to alter their weight ...
... majority function ( i.e. , H ( U ) = +1 if the majority of the u ; = +1 ) , then the layered machine is a committee machine . The effect of training any of the TLUS in the first layer of a layered machine ( i.e. , to alter their weight ...
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 |