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 ...
Sivu 75
Suppose that a pattern Y belonging to category i is presented with the result that some discriminant , say the jth ( j + i ) , is larger than the ith . That is , the machine erroneously places Y in category j .
Suppose that a pattern Y belonging to category i is presented with the result that some discriminant , say the jth ( j + i ) , is larger than the ith . That is , the machine erroneously places Y in category j .
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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
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side solution space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |