Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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In the error - correction training procedures , the training patterns are presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the TLU with the desired response dictated by the ...
In the error - correction training procedures , the training patterns are presented to the trainable TLU one at a time for trial . The trial consists of comparing the actual response of the TLU with the desired response dictated by the ...
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Suppose that a pattern Y belonging to category i is presented with the result that some discriminant , say the jth ( ji ) , is larger than the ith . That is , the machine erroneously places Y in cate- gory j . The weight vectors used by ...
Suppose that a pattern Y belonging to category i is presented with the result that some discriminant , say the jth ( ji ) , is larger than the ith . That is , the machine erroneously places Y in cate- gory j . The weight vectors used by ...
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REFERENCES 1 Duda , R. O. , and R. C. Singleton : Training a Threshold Logic Unit with Imperfectly Classified Patterns , paper presented at 1964 WESCON , August 26-29 , 1964 . 2 Fix , E. , and J. L. Hodges , Jr .: Discriminatory ...
REFERENCES 1 Duda , R. O. , and R. C. Singleton : Training a Threshold Logic Unit with Imperfectly Classified Patterns , paper presented at 1964 WESCON , August 26-29 , 1964 . 2 Fix , E. , and J. L. Hodges , Jr .: Discriminatory ...
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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 |