Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 20
... region lies entirely within the region ) : It will be left as an exercise for the reader to verify that the decision regions of a linear machine are always convex . 2.5 Linear classifications of patterns Suppose we have a finite set X ...
... region lies entirely within the region ) : It will be left as an exercise for the reader to verify that the decision regions of a linear machine are always convex . 2.5 Linear classifications of patterns Suppose we have a finite set X ...
Sivu 67
... region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count the number of regions in weight space formed by N augmented pattern hyperplanes , we obtain the number of dichotomies of N d ...
... region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count the number of regions in weight space formed by N augmented pattern hyperplanes , we obtain the number of dichotomies of N d ...
Sivu 68
... regions since this is the same problem except one dimension lower . Now , each one of these R ( N − 1 , D 1 ) regions on the Nth hyper- plane divides one of the original R ( N − 1 , D ) regions in the D - dimensional space into two ...
... regions since this is the same problem except one dimension lower . Now , each one of these R ( N − 1 , D 1 ) regions on the Nth hyper- plane divides one of the original R ( N − 1 , D ) regions in the D - dimensional space into two ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |