Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The sign of g ( x ) can be evaluated by a threshold element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of ...
The sign of g ( x ) can be evaluated by a threshold element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of ...
Sivu 21
The decision regions R , and R2 are separated by a hyperplane decision surface defined by g ( x ) = 0 . WE Discriminant d * g ( x ) = E W.7 1 i = 1 + w d +1 : W ; f +1 or -1 : Response Summing device Threshold element wa Pattern w d + 1 ...
The decision regions R , and R2 are separated by a hyperplane decision surface defined by g ( x ) = 0 . WE Discriminant d * g ( x ) = E W.7 1 i = 1 + w d +1 : W ; f +1 or -1 : Response Summing device Threshold element wa Pattern w d + 1 ...
Sivu 128
Moreover , T can always be selected so that the first p1 diagonal elements of A are positive , the next p2 diagonal elements are negative , and the last d – ( pı + pa ) diagonal elements are zero , where d is the order of A , and ( p1 + ...
Moreover , T can always be selected so that the first p1 diagonal elements of A are positive , the next p2 diagonal elements are negative , and the last d – ( pı + pa ) diagonal elements are zero , where d is the order of A , and ( p1 + ...
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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 |