Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Studies in Item Analysis and Prediction , " Stanford University Press , Stanford , California , 1961 . 6 Anderson , T. W .: “ Introduction to Multivariate Statistical Analysis , " John Wiley & Sons , Inc. , New York , 1958 .
Studies in Item Analysis and Prediction , " Stanford University Press , Stanford , California , 1961 . 6 Anderson , T. W .: “ Introduction to Multivariate Statistical Analysis , " John Wiley & Sons , Inc. , New York , 1958 .
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8 Widrow , B. , and M. E. Hoff : Adaptive Switching Circuits , Stanford Electronics Laboratories Technical Report 1553-1 , Stanford University , Stanford , California , June 30 , 1960 . 9 Widrow , B. , et al .
8 Widrow , B. , and M. E. Hoff : Adaptive Switching Circuits , Stanford Electronics Laboratories Technical Report 1553-1 , Stanford University , Stanford , California , June 30 , 1960 . 9 Widrow , B. , et al .
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7 Ridgway , W. C .: An Adaptive Logic System with Generalizing Properties , Stanford Electronics Laboratories Technical Report 1556-1 , prepared under Air Force Contract AF 33 ( 616 ) -7726 , Stanford University , Stanford , California ...
7 Ridgway , W. C .: An Adaptive Logic System with Generalizing Properties , Stanford Electronics Laboratories Technical Report 1556-1 , prepared under Air Force Contract AF 33 ( 616 ) -7726 , Stanford University , Stanford , California ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable 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 selected separable shown side 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 |