Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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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 W1 2 Y3 W2 WS 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 W1 2 Y3 W2 WS FIGURE 6.3 A commmittee of weight vectors way that ...
Sivu 99
... majority " committee machine with a fixed vote - taking TLU . * 1 x X Response Vote - taking TLU ( second layer ) Pattern = +1 d + 1 P committee TLUS ( first layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee ...
... majority " committee machine with a fixed vote - taking TLU . * 1 x X Response Vote - taking TLU ( second layer ) Pattern = +1 d + 1 P committee TLUS ( first layer ) FIGURE 6.4 A committee machine 6.3 A training procedure for committee ...
Sivu 102
... majority make positive dot products with each of the pattern vectors Y1 , Y2 , Ys ; then , adjustments to the weight vector ( s ) are made whenever Nx < 0. ( The reader could assume , for example , that Y1 contains Y2 and that Y2 ...
... majority make positive dot products with each of the pattern vectors Y1 , Y2 , Ys ; then , adjustments to the weight vector ( s ) are made whenever Nx < 0. ( The reader could assume , for example , that Y1 contains Y2 and that Y2 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |