Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 19
Sivu 5
... denote the set of R ; x2 Rs 3 R 2 OR R , The point ( 5 , -3 ) FIGURE 1.2 Point sets in E2 which map into category ... denoted by one of the symbols R1 , R2 , RR . As an example , consider the sets shown in Fig . 1.2 where d = 2 and R = 3 ...
... denote the set of R ; x2 Rs 3 R 2 OR R , The point ( 5 , -3 ) FIGURE 1.2 Point sets in E2 which map into category ... denoted by one of the symbols R1 , R2 , RR . As an example , consider the sets shown in Fig . 1.2 where d = 2 and R = 3 ...
Sivu 89
... denote each of the R 1 vectors in Z generated by Y by the symbol Z . , ( Y ) , j = 1 , " R , ji . 3. Let the ith block of D components of each Z . ; ( Y ) be set equal to Y for j = 1 , R , ji . ... " 4. For each Zil , ( Y ) , j = 1 ...
... denote each of the R 1 vectors in Z generated by Y by the symbol Z . , ( Y ) , j = 1 , " R , ji . 3. Let the ith block of D components of each Z . ; ( Y ) be set equal to Y for j = 1 , R , ji . ... " 4. For each Zil , ( Y ) , j = 1 ...
Sivu 110
... denoted by u , and let the weight vector corresponding to this TLU be denoted by W. For any given augmented input pattern Y each u1 = +1 or -1 , depending on whether Y. W ; is greater than or less than zero . Let us denote the dot ...
... denoted by u , and let the weight vector corresponding to this TLU be denoted by W. For any given augmented input pattern Y each u1 = +1 or -1 , depending on whether Y. W ; is greater than or less than zero . Let us denote the dot ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 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 |