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 |
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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 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 theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |