Department of Accounting and Information Systems

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Browsing Department of Accounting and Information Systems by Author "Fan, Weiguo"

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    Spline function smooth support vector machine for classification
    Yuan, Yubo; Fan, Weiguo; Pu, Dongmei (American Institute of Mathematical Sciences, 2007-08)
    This paper presents a duality theory for solving concave minimization problem and nonconvex quadratic programming problem subjected to nonlinear inequality constraints. By use of the canonical dual transformation developed recently, two canonical dual problems are formulated, respectively. These two dual problems are perfectly dual to the primal problems with zero duality gap. It is proved that the sufficient conditions for global minimizers and local extrema (both minima and maxima) are controlled by the triality theory discovered recently [5]. This triality theory can be used to develop certain useful primal-dual methods for solving difficult nonconvex minimization problems. Results shown that the difficult quadratic minimization problem with quadratic constraint can be converted into a one-dimensional dual problem, which can be solved completely to obtain all KKT points and global minimizer.
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    A Surrogate-based Generic Classifier for Chinese TV Series Reviews
    Ma, Yufeng; Xia, Long; Shen, Wenqi; Zhou, Mi; Fan, Weiguo (2016-11-21)
    With the emerging of various online video platforms like Youtube, Youku and LeTV, online TV series' reviews become more and more important both for viewers and producers. Customers rely heavily on these reviews before selecting TV series, while producers use them to improve the quality. As a result, automatically classifying reviews according to different requirements evolves as a popular research topic and is essential in our daily life. In this paper, we focused on reviews of hot TV series in China and successfully trained generic classifiers based on eight predefined categories. The experimental results showed promising performance and effectiveness of its generalization to different TV series.