Online Learning Based on Online DCA and Application to Online Classification
We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate...
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| Veröffentlicht in: | Neural computation Jg. 32; H. 4; S. 759 |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
01.04.2020
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| ISSN: | 1530-888X, 1530-888X |
| Online-Zugang: | Weitere Angaben |
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| Zusammenfassung: | We investigate an approach based on DC (Difference of Convex functions) programming and DCA (DC Algorithm) for online learning techniques. The prediction problem of an online learner can be formulated as a DC program for which online DCA is applied. We propose the two so-called complete/approximate versions of online DCA scheme and prove their logarithmic/sublinear regrets. Six online DCA-based algorithms are developed for online binary linear classification. Numerical experiments on a variety of benchmark classification data sets show the efficiency of our proposed algorithms in comparison with the state-of-the-art online classification algorithms. |
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| Bibliographie: | content type line 23 SourceType-Scholarly Journals-1 ObjectType-Correspondence-1 |
| ISSN: | 1530-888X 1530-888X |
| DOI: | 10.1162/neco_a_01266 |