Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 20
Sivu 5
... denote both the pattern point and the numbers x1 , x2 , " 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 ...
... denote both the pattern point and the numbers x1 , x2 , " 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 ...
Sivu 89
... denote each of the R 1 vectors in Z generated by Y by the symbol Z . ; ( Y ) , j = 1 , . . R , ji . " 3. Let the ith block of D components of each Z ;; ( Y ) be set equal to Y for j = 1 , . . . , R , j # i . 4. For each Z . ; ( Y ) , j ...
... denote each of the R 1 vectors in Z generated by Y by the symbol Z . ; ( Y ) , j = 1 , . . R , ji . " 3. Let the ith block of D components of each Z ;; ( Y ) be set equal to Y for j = 1 , . . . , R , j # i . 4. For each Z . ; ( Y ) , j ...
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 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 zero . Let us denote the dot ...
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 |