Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 24
... define a minimum - distance classifier with respect to the point sets P1 , P2 , = 1 , ... 9 9 PR as one which places each pattern X into the category associated with the closest point set . For each i = 1 , R , we define the functions ...
... define a minimum - distance classifier with respect to the point sets P1 , P2 , = 1 , ... 9 9 PR as one which places each pattern X into the category associated with the closest point set . For each i = 1 , R , we define the functions ...
Sivu 53
... define and use the following 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 ...
... define and use the following 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 ...
Sivu 128
... define the real , diagonal matrices D1 = λι 0 and D , = ( A - 4 ) 0 where A1 , ... 0 λp , are the first p1 diagonal elements of A , and Xp1 + 1 , Ap12 are the next p2 diagonal elements of A. Now let T1 be a d X p1 matrix consisting of ...
... define the real , diagonal matrices D1 = λι 0 and D , = ( A - 4 ) 0 where A1 , ... 0 λp , are the first p1 diagonal elements of A , and Xp1 + 1 , Ap12 are the next p2 diagonal elements of A. Now let T1 be a d X p1 matrix consisting of ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |