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Online Learning Based on Online DCA and Application to Online Classification

Abstract : 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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Submitted on : Monday, March 9, 2020 - 12:46:30 AM
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Hoai An Le Thi, Vinh Thanh Ho. Online Learning Based on Online DCA and Application to Online Classification. Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2020, 32 (4), pp.759-793. ⟨10.1162/neco_a_01266⟩. ⟨hal-02502129⟩



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