Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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where Ni is the number of patterns in the training subset X ;; ( X ) ; is called the sample mean ( or center of gravity ) of the ith category , and ( E ) ; is called the sample covariance matrix of the ith category .
where Ni is the number of patterns in the training subset X ;; ( X ) ; is called the sample mean ( or center of gravity ) of the ith category , and ( E ) ; is called the sample covariance matrix of the ith category .
Sivu 60
By expanding the exponent in Eq . ( 3:46 ) , it is a straightforward matter to identify the mean vector and covariance ... + * K ( x + 3 ) Ky = * ( x + ) ' ( 3:49 ) N and the sample mean Xi N ( 3.50 ) 1 60 PARAMETRIC TRAINING METHODS.
By expanding the exponent in Eq . ( 3:46 ) , it is a straightforward matter to identify the mean vector and covariance ... + * K ( x + 3 ) Ky = * ( x + ) ' ( 3:49 ) N and the sample mean Xi N ( 3.50 ) 1 60 PARAMETRIC TRAINING METHODS.
Sivu 61
and the sample mean Xi N ( 3.50 ) 1 The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . , Xn } ) is then given by Eq . ( 3.31 ) with M replaced by yn and replaced by + Ky . The process of obtaining ...
and the sample mean Xi N ( 3.50 ) 1 The optimum a posteriori discriminant function ( after training on the set { X1 , X2 , . , Xn } ) is then given by Eq . ( 3.31 ) with M replaced by yn and replaced by + Ky . The process of obtaining ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable 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 selected separable shown side 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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |