Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu vii
... theory is not yet generally appreciated by many researchers in the computer - related fields . Con- tributions to this theory have come from many disciplines including statistics , switching theory , physiological psychology , and ...
... theory is not yet generally appreciated by many researchers in the computer - related fields . Con- tributions to this theory have come from many disciplines including statistics , switching theory , physiological psychology , and ...
Sivu 44
... theory Statistical decision theory can be used as a means to establish the dis- criminant functions for probabilistic patterns governed by known proba- bility functions . Central to the decision - theoretic treatment is the specifi ...
... theory Statistical decision theory can be used as a means to establish the dis- criminant functions for probabilistic patterns governed by known proba- bility functions . Central to the decision - theoretic treatment is the specifi ...
Sivu 126
... Theory , vol . 178 , no . 5 , pp . S82 - S91 , September , 1962 . 5 Firschein , O. , and M. Fischler : Automatic Subclass Determination for Pattern- recognition Applications , Trans . IEEE on Elect . Computers , vol . EC - 12 , no . 2 ...
... Theory , vol . 178 , no . 5 , pp . S82 - S91 , September , 1962 . 5 Firschein , O. , and M. Fischler : Automatic Subclass Determination for Pattern- recognition Applications , Trans . IEEE on Elect . Computers , vol . EC - 12 , no . 2 ...
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 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 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 subsets X1 subsidiary discriminant functions Suppose terns 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 |