Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 26
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... probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ...
... probability - density function In the example of Sec . 3.5 , we assumed that the pattern components were statistically independent , binary , random variables . Such an assumption permitted a straightforward calculation of the ...
Sivu 118
... probability of error . This is so because the underlying probability distributions may be sufficiently overlapping that the optimum decision surfaces do not perfectly separate the training subsets . Consider , for example , the ...
... probability of error . This is so because the underlying probability distributions may be sufficiently overlapping that the optimum decision surfaces do not perfectly separate the training subsets . Consider , for example , the ...
Sivu 136
... probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate , 54 Novikoff , 92 , 93 Null category , 3 Number of linear dichotomies , 32 , 67 Okajima , 125 , 126 Optimum classifier , for binary pat ...
... probability - density function , bivariate , 50 equations of , 51 , 52 , 53 , 54 multivariate , 54 Novikoff , 92 , 93 Null category , 3 Number of linear dichotomies , 32 , 67 Okajima , 125 , 126 Optimum classifier , for binary pat ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |