Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 3
Sivu 77
... Cornell University . 8 Proof that these training procedures will either terminate or con- verge are given in Chapter ... Cornell Aeronautical Laboratory Report 85-460-1 , January , 1957 . 6 " Principles of Neurodynamics : Perceptrons and ...
... Cornell University . 8 Proof that these training procedures will either terminate or con- verge are given in Chapter ... Cornell Aeronautical Laboratory Report 85-460-1 , January , 1957 . 6 " Principles of Neurodynamics : Perceptrons and ...
Sivu 78
... Laboratories Technical Report 1553-1 , Stanford University , Stanford , California , June 30 , 1960 . 9 Widrow , B ... Cornell Aeronautical Laboratory Report VG - 1196 - G - 4 , Buffalo , New York , February , 1960 . CHAPTER 5 TRAINING ...
... Laboratories Technical Report 1553-1 , Stanford University , Stanford , California , June 30 , 1960 . 9 Widrow , B ... Cornell Aeronautical Laboratory Report VG - 1196 - G - 4 , Buffalo , New York , February , 1960 . CHAPTER 5 TRAINING ...
Sivu 93
... Cornell University . Our proof is a version of Kesler's as it was related to the author during discussions in July ... Aeronautical Laboratory Report VG - 1196 - G - 4 , Buffalo , New York , February , 1960 . 2 Joseph , R. D .: Contributions ...
... Cornell University . Our proof is a version of Kesler's as it was related to the author during discussions in July ... Aeronautical Laboratory Report VG - 1196 - G - 4 , Buffalo , New York , February , 1960 . 2 Joseph , R. D .: Contributions ...
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 d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges 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 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 |