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 Elec- tronics 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 Elec- tronics 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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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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |