Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 23
Sivu 16
... consider first the family of discriminant functions of the form g ( X ) = W1X1 + W2X2 + • + waxa + wa + 1 ( 2.2 ) This function is a linear function of the components of X ; we shall denote discriminant functions of this form by the ...
... consider first the family of discriminant functions of the form g ( X ) = W1X1 + W2X2 + • + waxa + wa + 1 ( 2.2 ) This function is a linear function of the components of X ; we shall denote discriminant functions of this form by the ...
Sivu 24
... consider those of a minimum - distance classifier with respect to point sets . i = ངག • Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each .. , R , let the ith point set consist of the L ; points P , ( 1 ) , P ...
... consider those of a minimum - distance classifier with respect to point sets . i = ངག • Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each .. , R , let the ith point set consist of the L ; points P , ( 1 ) , P ...
Sivu 32
... consider the case N = 4 , d 2 as an example . Figure 2-9a shows four points in a two- dimensional space . The lines li , i = 1 , 7 effect all possible linear partitions of these four points . Consider l , in particular . It could be the ...
... consider the case N = 4 , d 2 as an example . Figure 2-9a shows four points in a two- dimensional space . The lines li , i = 1 , 7 effect all possible linear partitions of these four points . Consider l , in particular . It could be the ...
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 |