Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu viii
... Stanford University and the University of California , Berkeley , in 1962 and 1964 , respectively . Professors N. Abramson and T. Cover of Stanford and L. Zadeh of the University of California gave many helpful suggestions for improving ...
... Stanford University and the University of California , Berkeley , in 1962 and 1964 , respectively . Professors N. Abramson and T. Cover of Stanford and L. Zadeh of the University of California gave many helpful suggestions for improving ...
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 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |