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O. Chapelle, V. Sindhwani and S. S. Keerthi, “Optimization Techniques for Semi-Supervised Support Vector Machines,” Journal of Machine Learning Research, Vol. 9, 2008, pp. 203-233.

has been cited by the following article:

• TITLE: An Inexact Implementation of Smoothing Homotopy Method for Semi-Supervised Support Vector Machines

  AUTHORS: Huijuan Xiong, Feng Shi

  KEYWORDS: Semi-Supervised Classification; Support Vector Machines; Truncated Smoothing Technique; Global Convergence

  JOURNAL NAME: Journal of Data Analysis and Information Processing, Vol.1 No.1, February 26, 2013

  ABSTRACT: Semi-supervised Support Vector Machines is an appealing method for using unlabeled data in classification. Smoothing homotopy method is one of feasible method for solving semi-supervised support vector machines. In this paper, an inexact implementation of the smoothing homotopy method is considered. The numerical implementation is based on a truncated smoothing technique. By the new technique, many “non-active” data can be filtered during the computation of every iteration so that the computation cost is reduced greatly. Besides this, the global convergence can make better local minima and then result in lower test errors. Final numerical results verify the efficiency of the method.