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Using the Nonsmooth Analysis in a Learning Process
- 1.0355020 - ÚI 2011 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Jiřina, Marcel - Jiřina jr., M.
Using the Nonsmooth Analysis in a Learning Process.
Proceeding of The 2010 IRAST International Congress on Computer Applications and Computational Science. -: IRAST, 2010 - (Chellappan, S.; Cheng, A.; Min, M.; Quang, V.; Ramalingam, P.; Yang, H.), s. 91-94. ISBN 978-981-08-6846-8.
[CACS 2010. 2010 IRAST International Congress on Computer Applications and Computational Science. Singapore (SG), 04.12.2010--06.12.2010]
Grant CEP: GA MŠMT(CZ) 1M0567
Výzkumný záměr: CEZ:AV0Z10300504
Klíčová slova: nonsmooth analysis * pattern classification * multivariate systems * 1-NN classifier * weighted distances
Kód oboru RIV: BB - Aplikovaná statistika, operační výzkum
We propose an unconventional updating algorithm for weighting features for a classification task with abrupt changes of the error function. Using a so-called nonsmooth analysis we prove the quadratic convergence of such a learning process. For the dynamic optimization of the step size we use approach similar to Runge’s method of a half step. Finally we demonstrate its classification abilities on artificial as well as on real-life classification tasks.
Trvalý link: http://hdl.handle.net/11104/0193878
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