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.

中文翻译：

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基于在线 DCA 的在线学习及其在在线分类中的应用

我们研究了一种基于 DC（凸函数差）编程和 DCA（DC 算法）的在线学习技术方法。在线学习者的预测问题可以表述为应用在线 DCA 的 DC 程序。我们提出了两种所谓的在线 DCA 方案的完整/近似版本，并证明了它们的对数/次线性遗憾。为在线二元线性分类开发了六种基于在线 DCA 的算法。在各种基准分类数据集上的数值实验表明，与最先进的在线分类算法相比，我们提出的算法的效率。