Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 49
... estimates for the unknown probabili- 1 2 * The reader with background in statistics will recall that there are circumstances in which it is possible to make optimum estimates of unknown probability values . These optimum estimates are ...
... estimates for the unknown probabili- 1 2 * The reader with background in statistics will recall that there are circumstances in which it is possible to make optimum estimates of unknown probability values . These optimum estimates are ...
Sivu 58
... estimates of M , and 2 , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i An expression that is somewhat simpler than the one in Eq . ( 3 · 36 ) can be ...
... estimates of M , and 2 , respectively . The use of these estimates to specify the discriminant functions would constitute a para- metric training method . i An expression that is somewhat simpler than the one in Eq . ( 3 · 36 ) can be ...
Sivu 120
... estimate the values of p ( xi ) p ( i ) for i = 1 , . . . , R around the point X. If these values are approximated by ... estimates ) . If the training subsets are large , it has been shown that the Fix and Hodges decision rule leads to ...
... estimate the values of p ( xi ) p ( i ) for i = 1 , . . . , R around the point X. If these values are approximated by ... estimates ) . If the training subsets are large , it has been shown that the Fix and Hodges decision rule leads to ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |