Boliang Sun

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In this paper, we propose a dual perspective of online learning algorithm, which concerns using a window method to achieve sparsity and robustness. It makes use of Fenchel conjugates and gradient ascent to perform online learning optimization process. The window method is an update strategy for the classifier. It consists of two bounds which related to the(More)
In this paper, we propose a method to detect abnormal events using a novel unsupervised kernel learning algorithm. The key of our method is to learn a suitable feature space and the associated kernel function of the training samples. By considering the self-similarity property of training samples, we assume that the training samples will show the distinctly(More)
We propose a novel online coregularization framework for multiview semisupervised learning based on the notion of duality in constrained optimization. Using the weak duality theorem, we reduce the online coregularization to the task of increasing the dual function. We demonstrate that the existing online coregularization algorithms in previous work can be(More)
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