Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Thus , each point in the pattern space is transformed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call the first image space or the I , space . The transformation between the pattern space and the ...
Thus , each point in the pattern space is transformed into one of the vertices of a Pi - dimensional hypercube . This hypercube we shall call the first image space or the I , space . The transformation between the pattern space and the ...
Sivu 105
6.6b refer to the patterns which are transformed into each of these image points . Note that the planes in Fig . 6.6a divide the pattern space into compartments or cells . In this example there are four cells that contain pattern points ...
6.6b refer to the patterns which are transformed into each of these image points . Note that the planes in Fig . 6.6a divide the pattern space into compartments or cells . In this example there are four cells that contain pattern points ...
Sivu 131
A.3 Transformation of normal patterns Consider the normal distribution expressed by 1 p ( X ) ( 27 ) d / 28 | exp { -12 [ ( X – M ) ' E - ' ( X – M ) ] } [ X – M ) ] ( A.10 ) / Σ - 1 where 2 is the covariance matrix and M is the mean ...
A.3 Transformation of normal patterns Consider the normal distribution expressed by 1 p ( X ) ( 27 ) d / 28 | exp { -12 [ ( X – M ) ' E - ' ( X – M ) ] } [ X – M ) ] ( A.10 ) / Σ - 1 where 2 is the covariance matrix and M is the mean ...
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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 |