Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 98
Sivu 223
... patterns are correctly classified by the system . Results showed that two pattern points in 2 , namely , ( 9 , 5 ) and ( 10 , 7 ) , were misclassified as w1 ; and four pattern points in w1 , namely ( 17 , 21 ) , ( 18 , 20 ) , ( 18 , 23 ) ...
... patterns are correctly classified by the system . Results showed that two pattern points in 2 , namely , ( 9 , 5 ) and ( 10 , 7 ) , were misclassified as w1 ; and four pattern points in w1 , namely ( 17 , 21 ) , ( 18 , 20 ) , ( 18 , 23 ) ...
Sivu 256
... point patterns is the search for unusual points or unusual point configurations. This includes the issue of outliers, i.e. points appearing at locations where they are not expected according to the construction principles of the pattern ...
... point patterns is the search for unusual points or unusual point configurations. This includes the issue of outliers, i.e. points appearing at locations where they are not expected according to the construction principles of the pattern ...
Sivu 357
... points in a pattern . For this reason , the Gibbs process is simulated by trial and error , starting with a suitable initial pattern . Points are removed at random and are replaced only by points that are likely to occur , given the ...
... points in a pattern . For this reason , the Gibbs process is simulated by trial and error , starting with a suitable initial pattern . Points are removed at random and are replaced only by points that are likely to occur , given the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 |