Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 20
Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classifier , the surface Sij is the hyperplane which ...
Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classifier , the surface Sij is the hyperplane which ...
Sivu 23
where w = Σ W ; 2 1 = 1 Note from Fig . 2.5 that the absolute value of n . P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wd + 1 // wl .
where w = Σ W ; 2 1 = 1 Note from Fig . 2.5 that the absolute value of n . P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wd + 1 // wl .
Sivu 39
Note the pronounced threshold effect , for large M + 1 , around X = 2 . Also note that for each value of M P2 ( M + 1 ) , M = = 22 ( 2:45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 276 ) ( M + 1 ) ...
Note the pronounced threshold effect , for large M + 1 , around X = 2 . Also note that for each value of M P2 ( M + 1 ) , M = = 22 ( 2:45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 276 ) ( M + 1 ) ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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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 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 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 selection separable shown side solution 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 |