Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 40
... Networks of Adaline " Neurons , " in Yovits , Jacobi , and Goldstein ( eds . ) , " Self - organizing Systems -1962 , " p . 442 , Spartan Books , Washington , D.C. , 1962 . 13 Brown , R .: Logical Properties of Adaptive Networks ...
... Networks of Adaline " Neurons , " in Yovits , Jacobi , and Goldstein ( eds . ) , " Self - organizing Systems -1962 , " p . 442 , Spartan Books , Washington , D.C. , 1962 . 13 Brown , R .: Logical Properties of Adaptive Networks ...
Sivu 94
... Agmon , S .: The Relaxation Method for Linear Inequalities , Canadian Journal of Mathematics , vol . 6 , no . 3 , pp . 382-392 , 1954 . CHAPTER 6 LAYERED MACHINES 6.1 Layered networks of TLUS Networks 94 TRAINING THEOREMS.
... Agmon , S .: The Relaxation Method for Linear Inequalities , Canadian Journal of Mathematics , vol . 6 , no . 3 , pp . 382-392 , 1954 . CHAPTER 6 LAYERED MACHINES 6.1 Layered networks of TLUS Networks 94 TRAINING THEOREMS.
Sivu 95
... network . The properties of TLU networks are not yet fully understood . ( For example , expressions do not yet exist for the capacity of these networks nor training theorems for them . ) Nevertheless , it is believed that these networks ...
... network . The properties of TLU networks are not yet fully understood . ( For example , expressions do not yet exist for the capacity of these networks nor training theorems for them . ) Nevertheless , it is believed that these networks ...
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 |