Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 24
Let us define the Euclidean distance d ( X , P :) from an arbitrary point X to the point set Pi by 2 = d ( X , P :) min j = 1 , ... , Li | X – P ; ( ) ( 2:16 ) > That is , the distance between X and Pi is the smallest of the distances ...
Let us define the Euclidean distance d ( X , P :) from an arbitrary point X to the point set Pi by 2 = d ( X , P :) min j = 1 , ... , Li | X – P ; ( ) ( 2:16 ) > That is , the distance between X and Pi is the smallest of the distances ...
Sivu 47
... to illustrate the use of the concepts developed above . We shall assume that ( ilj ) is a symmetrical loss function ; that is , ( 11 ) = 0,1 ( 1/2 ) = 1,1 ( 2/1 ) = 1 , and ^ ( 2/2 ) = 0. As in Chapter 1 , we define the discriminant ...
... to illustrate the use of the concepts developed above . We shall assume that ( ilj ) is a symmetrical loss function ; that is , ( 11 ) = 0,1 ( 1/2 ) = 1,1 ( 2/1 ) = 1 , and ^ ( 2/2 ) = 0. As in Chapter 1 , we define the discriminant ...
Sivu 128
Let us now define the real , diagonal matrices λ , 0 Di 0 and ( A.4 ) - Ap + 1 0 D2 = 0 -pitpad where λι , > 1p , are the first pı diagonal elements of A , and 1p1 + 1 , Apa + p , are the next pa diagonal elements of A. Now let T be ...
Let us now define the real , diagonal matrices λ , 0 Di 0 and ( A.4 ) - Ap + 1 0 D2 = 0 -pitpad where λι , > 1p , are the first pı diagonal elements of A , and 1p1 + 1 , Apa + p , are the next pa diagonal elements of A. Now let T be ...
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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 |