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 , . . . , 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 9 ...
... define a minimum - distance classifier with respect to the point sets P1 , P2 , . . . , 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 9 ...
Sivu 47
... define the discriminant function , g ( X ) = g1 ( X ) — 92 ( X ) . If g ( X ) > 0 , the machine places X in category ... define p ( xi = PARAMETRIC TRAINING METHODS 47 An example,
... define the discriminant function , g ( X ) = g1 ( X ) — 92 ( X ) . If g ( X ) > 0 , the machine places X in category ... define p ( xi = PARAMETRIC TRAINING METHODS 47 An example,
Sivu 53
... define and use the following matrices . Let the pattern vector X be a column vector ( a 2 × 1 matrix ) with compo- Category 3 Category 2 Category 1 Category 4 FIGURE 3.3 Ellipsoidal clusters of patterns nents x1 and x2 . Similarly , let ...
... define and use the following matrices . Let the pattern vector X be a column vector ( a 2 × 1 matrix ) with compo- Category 3 Category 2 Category 1 Category 4 FIGURE 3.3 Ellipsoidal clusters of patterns nents x1 and x2 . Similarly , let ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |