Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
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 ...
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... Stanford University Libraries 3 6105 031 475 358 9335 N5 . Cop . 2 Math & Computer Sciences Library ( 415 ) 723-4672 Stanford University Stanford , CA 94305 DATE DUE ... STANFORD UNIVERSITY LIBRARIES STANFORD , CALIFORNIA 94305-6004 46570.
... Stanford University Libraries 3 6105 031 475 358 9335 N5 . Cop . 2 Math & Computer Sciences Library ( 415 ) 723-4672 Stanford University Stanford , CA 94305 DATE DUE ... STANFORD UNIVERSITY LIBRARIES STANFORD , CALIFORNIA 94305-6004 46570.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges 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 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 second layer shown in Fig solution weight vectors 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 |