Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 35
Sivu 29
... components f1 , f2 , , fM are functions d . The first d components of F are x1 , x22 , 1 ) / 2 components are all the pairs x1x2 , X1X 3 , = 1 , of the xi , i xa2 ; the next d ( d · — " Xa - 1a ; the last d components are x1 , x2 , • ω1 ...
... components f1 , f2 , , fM are functions d . The first d components of F are x1 , x22 , 1 ) / 2 components are all the pairs x1x2 , X1X 3 , = 1 , of the xi , i xa2 ; the next d ( d · — " Xa - 1a ; the last d components are x1 , x2 , • ω1 ...
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 111
... components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ... components , and G2 ( X ) is a vector with 2PL components . Let the ith component of G1 ( X ) be denoted by g1 ) ( X ) ...
... components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ... components , and G2 ( X ) is a vector with 2PL components . Let the ith component of G1 ( X ) be denoted by g1 ) ( X ) ...
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 |