Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 49
Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of wd + ... that there are circumstances in which it is possible to make optimum estimates of unknown probability values .
Note , for example , that the values of the a priori probabilities p ( 1 ) and 1 – p ( 1 ) affect only the value of wd + ... that there are circumstances in which it is possible to make optimum estimates of unknown probability values .
Sivu 54
There is a multivariate normal distribution which describes the joint probability density of d components . Patterns selected according to this joint probability distribution will be called multivariate normal patterns or , more simply ...
There is a multivariate normal distribution which describes the joint probability density of d components . Patterns selected according to this joint probability distribution will be called multivariate normal patterns or , more simply ...
Sivu 136
... 76 of piecewise linear machines , 116 , 122 of TLUs , 65 Nonredundant partition , 107 Normal patterns , 52 , 54 mean vector of , 53 , 54 transformation of , 131 Normal probability - density function , bivariate , 50 equations of ...
... 76 of piecewise linear machines , 116 , 122 of TLUs , 65 Nonredundant partition , 107 Normal patterns , 52 , 54 mean vector of , 53 , 54 transformation of , 131 Normal probability - density function , bivariate , 50 equations of ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative 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 solution space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |