Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 37
Sivu 29
... components fi , f2 , ... , fм are functions of the x , i = 1 , . . Xi , d . The first d components of F are x1 , x22 , x2 ; the next d ( d − 1 ) / 2 components are all the pairs x1x2 , x1X3 , Xa - 1a ; the last d components are x1 , x2 ...
... components fi , f2 , ... , fм are functions of the x , i = 1 , . . Xi , d . The first d components of F are x1 , x22 , x2 ; the next d ( d − 1 ) / 2 components are all the pairs x1x2 , x1X3 , Xa - 1a ; the last d components are x1 , x2 ...
Sivu 50
... components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the discriminant function for the optimum classifying machine . The optimum classifier can also be ...
... components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the discriminant function for the optimum classifying machine . The optimum classifier can also be ...
Sivu 90
... components is set equal to -Ŷ , and whose other components are all equal to zero . We apply this rule to each element of Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight ...
... components is set equal to -Ŷ , and whose other components are all equal to zero . We apply this rule to each element of Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |