Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
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 ...
Sivu 113
... Stanford Electronics Laboratories Technical Report 1556-1 , prepared under Air Force Contract AF 33 ( 616 ) -7726 , Stanford LAYERED MACHINES 113 Bibliographical and historical remarks, References, PIECEWISE LINEAR MACHINES.
... Stanford Electronics Laboratories Technical Report 1556-1 , prepared under Air Force Contract AF 33 ( 616 ) -7726 , Stanford LAYERED MACHINES 113 Bibliographical and historical remarks, References, PIECEWISE LINEAR MACHINES.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
LAYERED MACHINES | 95 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X gi(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 parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying 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 vector Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 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 |