Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 23
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 . ( If Aw > 0 , the origin is on the positive side of the hyperplane .
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 . ( If Aw > 0 , the origin is on the positive side of the hyperplane .
Sivu 85
That is , W.Y > 0 for all W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at ...
That is , W.Y > 0 for all W in W and each Y in y ' Here W is an open convex region bounded by hyperplanes ( the pattern hyperplanes ) all of which pass through the origin . Such a region is called a convex polyhedral cone with vertex at ...
Sivu 105
The three - dimensional I , space is then a cube , centered about the origin , whose vertices represent the eight possible combinations of responses of three TLUs . This cube is shown in Fig . 6:66 . If we number the coordinate axes of ...
The three - dimensional I , space is then a cube , centered about the origin , whose vertices represent the eight possible combinations of responses of three TLUs . This cube is shown in Fig . 6:66 . If we number the coordinate axes of ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
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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 |