Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 22
Sivu 24
... define the Euclidean distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by d ( X , Pi ) = min j = 1 , ... , Li X - − P¿ ‹ 3 ) | ( 2.16 ) That is , the distance between X and P ; is the smallest of the distances ...
... define the Euclidean distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by d ( X , Pi ) = min j = 1 , ... , Li X - − P¿ ‹ 3 ) | ( 2.16 ) That is , the distance between X and P ; is the smallest of the distances ...
Sivu 53
... define and use the following matrices . Let the pattern vector X be a column vector ( a 2 × 1 matrix ) with compo- Category 2 * 2 Category 3 Category 1 Category 4 FIGURE 3.3 Ellipsoidal clusters of patterns nents x and x2 . Similarly ...
... define and use the following matrices . Let the pattern vector X be a column vector ( a 2 × 1 matrix ) with compo- Category 2 * 2 Category 3 Category 1 Category 4 FIGURE 3.3 Ellipsoidal clusters of patterns nents x and x2 . Similarly ...
Sivu 128
... define the real , diagonal matrices λι 0 D1 = and where A1 , ... 9 D2 = − λp1 + 1 Χρι 0 ( A - 4 ) 0 λp , are the first p1 diagonal elements of A , and Ap1 + 1 , Api + p , are the next p2 diagonal elements of A. Now let T1 be a d X p1 ...
... define the real , diagonal matrices λι 0 D1 = and where A1 , ... 9 D2 = − λp1 + 1 Χρι 0 ( A - 4 ) 0 λp , are the first p1 diagonal elements of A , and Ap1 + 1 , Api + p , are the next p2 diagonal elements of A. Now let T1 be a d X p1 ...
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 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 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 |