Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 10
... estimates for X1 and X2 might then be the respective sample means ( centers of gravity ) of the patterns in each ... estimated , Eq . ( 1.2 ) could be used for the specifi- cation of g ( X ) , and the parametric training process would be ...
... estimates for X1 and X2 might then be the respective sample means ( centers of gravity ) of the patterns in each ... estimated , Eq . ( 1.2 ) could be used for the specifi- cation of g ( X ) , and the parametric training process would be ...
Sivu 49
... estimates for the unknown probabili- + * 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- + * 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 Σ , 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 Σ , 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 ...
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 step subsidiary discriminant Suppose terns 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 |