Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
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 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 ...
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... Stanford University Libraries ENGINEERING LIBRARY 335 N5 3 6105 030 196 286 Cop . 7 Stanford University Libraries Stanford , California Return this book on or before date due . AUG 18'72 JUL 8 1980 MAR 13 1974 APR 2.1974 JAN 2 0 197 FEB ...
... Stanford University Libraries ENGINEERING LIBRARY 335 N5 3 6105 030 196 286 Cop . 7 Stanford University Libraries Stanford , California Return this book on or before date due . AUG 18'72 JUL 8 1980 MAR 13 1974 APR 2.1974 JAN 2 0 197 FEB ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks 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 space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods 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 |