Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 63
... Stanford University Press , Stanford , California , 1961 . 6 Anderson , T. W .: " Introduction to Multivariate Statistical Analysis , " John Wiley & Sons , Inc. , New York , 1958 . 7 Kailath , T .: Correlation Detection of Signals ...
... Stanford University Press , Stanford , California , 1961 . 6 Anderson , T. W .: " Introduction to Multivariate Statistical Analysis , " John Wiley & Sons , Inc. , New York , 1958 . 7 Kailath , T .: Correlation Detection of Signals ...
Sivu 78
... Stanford Elec- tronics Laboratories Technical Report 1553-1 , Stanford University , Stanford , California , June 30 , 1960 . 9 Widrow , B. , et al .: Practical Applications for Adaptive Data - processing Systems , 1963 WESCON Paper 11.4 ...
... Stanford Elec- tronics Laboratories Technical Report 1553-1 , Stanford University , Stanford , California , June 30 , 1960 . 9 Widrow , B. , et al .: Practical Applications for Adaptive Data - processing Systems , 1963 WESCON Paper 11.4 ...
Sivu 94
... Stanford Electronics Laboratories Technical Report 1556-1 , prepared under Air Force Contract AF 33 ( 616 ) -7726 , Stanford University , Stanford , California , April , 1962 . 8 Motzkin , T. S. , and I. J. Schoenberg : The Relaxation ...
... Stanford Electronics Laboratories Technical Report 1556-1 , prepared under Air Force Contract AF 33 ( 616 ) -7726 , Stanford University , Stanford , California , April , 1962 . 8 Motzkin , T. S. , and I. J. Schoenberg : The Relaxation ...
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 |