Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 29
To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , fm are functions of the Xi , i = 1 , ... , d . The first d components of Fare x1 , x2 , .
To explain the more important implementation we first define the M - dimensional vector F whose components f1 , f2 , fm are functions of the Xi , i = 1 , ... , d . The first d components of Fare x1 , x2 , .
Sivu 90
inaccurately classified as a member of y , when it actually belongs to Yi ; Zx is then expressed by Zil ( 9x ) ( 5.33 ) Zk That is , Zx is a vector whose ith block of D components is set equal to Yk , whose Ith block of D components is ...
inaccurately classified as a member of y , when it actually belongs to Yi ; Zx is then expressed by Zil ( 9x ) ( 5.33 ) Zk That is , Zx is a vector whose ith block of D components is set equal to Yk , whose Ith block of D components is ...
Sivu 111
Since U is a binary vector with P components there are 2P distinct U vectors . For some of these U vectors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let H. be a matrix Hy whose rows consist of those U vectors for which H ...
Since U is a binary vector with P components there are 2P distinct U vectors . For some of these U vectors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let H. be a matrix Hy whose rows consist of those U vectors for which H ...
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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 |