Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 23
... origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) . ( If A > 0 , the origin is on the positive side of the hyperplane . ) The equation X. n + Aw = 0 ( 2.15 ) is said to be the ...
... origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) . ( If A > 0 , the origin is on the positive side of the hyperplane . ) The equation X. n + Aw = 0 ( 2.15 ) is said to be the ...
Sivu 85
... origin but at some point whose distance from the origin increases with increasing M and b . A two- dimensional example is shown in Fig . 5.1 . Let | W - Ŵ ; | 2 be the squared distance between some fixed interior point W in w ' and Ŵ ...
... origin but at some point whose distance from the origin increases with increasing M and b . A two- dimensional example is shown in Fig . 5.1 . Let | W - Ŵ ; | 2 be the squared distance between some fixed interior point W in w ' and Ŵ ...
Sivu 105
... Origin * 1 ( a ) Pattern space 1,4,5,8 TLU 3 Origin TLU 2 * 3,7 TLU 1 ( b ) Image space a FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from ...
... Origin * 1 ( a ) Pattern space 1,4,5,8 TLU 3 Origin TLU 2 * 3,7 TLU 1 ( b ) Image space a FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 , and 3 , we have an easy means of determining the trans- formation from ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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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 |