Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
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 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters partition pattern classifier pattern hyperplane pattern space pattern vector patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |