Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 25
Sivu 53
... matrices . Let the pattern vector X be a column vector ( a 2 X 1 matrix ) with compo- Category 2 22 Category 3 Category 1 Category 4 FIGURE 3.3 Ellipsoidal clusters of patterns nents x1 and x2 . Similarly , let the mean vector M be a ...
... matrices . Let the pattern vector X be a column vector ( a 2 X 1 matrix ) with compo- Category 2 22 Category 3 Category 1 Category 4 FIGURE 3.3 Ellipsoidal clusters of patterns nents x1 and x2 . Similarly , let the mean vector M be a ...
Sivu 58
... matrix . The first step in its de- velopment is to form a matrix Q ; whose columns are derived from the patterns in X. Subtract from each of the N ; patterns in X ; the sample- mean pattern ( X ) ;; Q ; is then d X N ; matrix whose N ...
... matrix . The first step in its de- velopment is to form a matrix Q ; whose columns are derived from the patterns in X. Subtract from each of the N ; patterns in X ; the sample- mean pattern ( X ) ;; Q ; is then d X N ; matrix whose N ...
Sivu 59
... matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean vector are the only unknown parameters of the dis- criminant function . For any known M , X will be normal with mean M and covariance matrix Σ . * That is , p ...
... matrix Σ ; and unknown mean vector . Thus , the d com- ponents of the mean vector are the only unknown parameters of the dis- criminant function . For any known M , X will be normal with mean M and covariance matrix Σ . * That is , p ...
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 |