Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... normal or Gaussian probability - density function is important because of its computational simplicity and because it represents a realistic model of many pattern - classification situations . Furthermore , normal distributions ...
... normal or Gaussian probability - density function is important because of its computational simplicity and because it represents a realistic model of many pattern - classification situations . Furthermore , normal distributions ...
Sivu 54
... normal distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns ...
... normal distribution which describes the joint probability density of d components . Patterns selected according to this joint proba- bility distribution will be called multivariate normal patterns or , more simply , normal patterns ...
Sivu 55
... normal patterns " We are now ready to derive the optimum classifier for normal patterns . We shall temporarily assume that for each category i , where i = 1 , R , we know the a priori probability p ( i ) and the particular d - variate ...
... normal patterns " We are now ready to derive the optimum classifier for normal patterns . We shall temporarily assume that for each category i , where i = 1 , R , we know the a priori probability p ( i ) and the particular d - variate ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |