Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 58
i where N 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 ( 2 ) : is called the sample covariance matrix of the ith category .
i where N 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 ( 2 ) : is called the sample covariance matrix of the ith category .
Sivu 59
derived from the training set as if they were the known means and covariance matrices . ... Suppose the pattern vectors belonging to category i are normal with known covariance matrix ? ; and unknown mean vector .
derived from the training set as if they were the known means and covariance matrices . ... Suppose the pattern vectors belonging to category i are normal with known covariance matrix ? ; and unknown mean vector .
Sivu 131
A. 3 Transformation of normal patterns Consider the normal distribution expressed by p ( X ) = 1 ( 2 ) / 2 21 exp { -22 [ ( x – M ) ' - ( X – M ) ] } ) Σ - 1 ( ( A.10 ) where I is the covariance matrix and M is the mean vector .
A. 3 Transformation of normal patterns Consider the normal distribution expressed by p ( X ) = 1 ( 2 ) / 2 21 exp { -22 [ ( x – M ) ' - ( X – M ) ] } ) Σ - 1 ( ( A.10 ) where I is the covariance matrix and M is the mean vector .
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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 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 machine linearly separable matrix 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 regions respect response rule sample mean selection separable shown side solution space Stanford step 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 |
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 |