Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 5
... consider the sets shown in Fig . 1.2 where d = 2 and R = 3. A point in the plane is mapped into the numbers 1 , 2 , or 3 , according to its membership in R1 , R2 , or R3 , respectively . For example , the pattern ( 5 , -3 ) would be ...
... consider the sets shown in Fig . 1.2 where d = 2 and R = 3. A point in the plane is mapped into the numbers 1 , 2 , or 3 , according to its membership in R1 , R2 , or R3 , respectively . For example , the pattern ( 5 , -3 ) would be ...
Sivu 16
... consider first the family of discriminant functions of the form W1X1 + W2X2 + + waxa + wa + 1 g ( X ) = ( 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 W1X1 + W2X2 + + waxa + wa + 1 g ( X ) = ( 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 = Suppose we are given R finite point sets P1 , 9 P2 , PR . For each 1 , . . . , R , let the ith point set consist of the L points P. ( 1 ) , P. ( 2 ) , P ...
... consider those of a minimum - distance classifier with respect to point sets . i = Suppose we are given R finite point sets P1 , 9 P2 , PR . For each 1 , . . . , R , let the ith point set consist of the L points P. ( 1 ) , P. ( 2 ) , P ...
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 step subsidiary discriminant Suppose terns 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 |