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 TLUS ( first layer ) Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the training set Y ...
... committee TLUS ( first layer ) Vote - taking TLU ( second layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee machines Suppose that we have training pattern subsets Y1 and Y2 , comprising the training set Y ...
Sivu 100
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Y. For ex- ample , if exactly seven TLUS in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven TLUs ...
... committee TLUS would have posi- tive responses , and the machine would respond correctly to Y. For ex- ample , if exactly seven TLUS in a committee of size nine had negative responses to Yk , then N -5 . At least three of the seven TLUs ...
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 |