Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 68
... solution weight point W , Solution region 3 W Pattern hyperplanes FIGURE 4.1 A two - dimensional weight space with three pattern hyperplanes encircled numbers attached to the hyperplanes indicate the number of the pattern . Thus , the ...
... solution weight point W , Solution region 3 W Pattern hyperplanes FIGURE 4.1 A two - dimensional weight space with three pattern hyperplanes encircled numbers attached to the hyperplanes indicate the number of the pattern . Thus , the ...
Sivu 72
... ( solution ) region . In this simple example , a solution occurs after five adjustments . Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however ...
... ( solution ) region . In this simple example , a solution occurs after five adjustments . Note that an adjustment to correct the response for one pattern may very well undo a correction made on a previous pattern . Eventually , however ...
Sivu 87
... solution weight vector when one exists . 5.5 A training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , 2 , . . . , YR . These subsets are linearly separable if ...
... solution weight vector when one exists . 5.5 A training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , 2 , . . . , YR . These subsets are linearly separable if ...
Sisältö
Preface vii | 11 |
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 important 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |