Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 5
... denote both the pattern point and the pattern vector by the symbol X. A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . . . , R. Let the symbol R ; denote the set of 2 R3 R2 R R -The ...
... denote both the pattern point and the pattern vector by the symbol X. A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , . . . , R. Let the symbol R ; denote the set of 2 R3 R2 R R -The ...
Sivu 110
... denoted by u . , and let the weight vector corresponding to this TLU be denoted by W. For any given augmented input pattern Y each . u = +1 or 1 , depending on whether Y. W ; is greater than or less than Ui zero . Let us denote the dot ...
... denoted by u . , and let the weight vector corresponding to this TLU be denoted by W. For any given augmented input pattern Y each . u = +1 or 1 , depending on whether Y. W ; is greater than or less than Ui zero . Let us denote the dot ...
Sivu 123
... denote the weight vector which is to be adjusted at this step by the symbol w ; [ k ] . [ The superscript ( j ) and the subscript i denote that this weight vector is the jth member of the ith bank . ] The adjusted weight vector wi [ k + ...
... denote the weight vector which is to be adjusted at this step by the symbol w ; [ k ] . [ The superscript ( j ) and the subscript i denote that this weight vector is the jth member of the ith bank . ] The adjusted weight vector wi [ k + ...
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 |