Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 98
... committee " of weight vectors W1 , W2 , and Wa in Fig . 6.3 . With respect to these weight vectors , we have the inequalities 1 1 W2 Y1 < 0 2 · W1 . Y1 > 0 1 2 W2 Y2 > 0 · W1 . Y2 > 0 1 W1 . Y2 > 0 3 W2 . Y3 < 0 ( 6.4 ) · W3 Y1 > 0 • W3 ...
... committee " of weight vectors W1 , W2 , and Wa in Fig . 6.3 . With respect to these weight vectors , we have the inequalities 1 1 W2 Y1 < 0 2 · W1 . Y1 > 0 1 2 W2 Y2 > 0 · W1 . Y2 > 0 1 W1 . Y2 > 0 3 W2 . Y3 < 0 ( 6.4 ) · W3 Y1 > 0 • W3 ...
Sivu 99
... committee machine with a fixed vote - taking TLU . x1 x2 Response X Pattern xd + 1 = +1 P committee TLUS ( first layer ) Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines ...
... committee machine with a fixed vote - taking TLU . x1 x2 Response X Pattern xd + 1 = +1 P committee TLUS ( first layer ) Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines ...
Sivu 100
... machine , one at a time , for trial . The procedure is similar to the error ... machine . Suppose that at the kth stage of the process a pattern Yk ... committee TLUS have negative responses . If the responses of at least . 11⁄2 ...
... machine , one at a time , for trial . The procedure is similar to the error ... machine . Suppose that at the kth stage of the process a pattern Yk ... committee TLUS have negative responses . If the responses of at least . 11⁄2 ...
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 |