Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 43
Sivu 9
... values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for the purpose of obtaining ...
... values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for the purpose of obtaining ...
Sivu 44
... values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 , ... , R. In such a ... values of the parameters of the p ( X | i ) and of the parameters p ( i ) . 2. The values of the parameters of the p ...
... values might also be unknown , are the a priori probabilities for each class p ( i ) , i = 1 , ... , R. In such a ... values of the parameters of the p ( X | i ) and of the parameters p ( i ) . 2. The values of the parameters of the p ...
Sivu 49
... values of the a priori proba- bilities p ( 1 ) and 1 - p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 response for all patterns ...
... values of the a priori proba- bilities p ( 1 ) and 1 - p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 response for all patterns ...
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 |