Abstract
Transductive support vector machine optimization problem is a NP problem, in the case of larger number of labeled samples, it is often difficult to obtain a global optimal solution, thereby the good generalization ability of transductive learning has been affected. Previous methods can not give consideration to both running efficiency and classification precision. In this paper, a transductive support vector machine algorithm based on ant colony optimization is proposed to overcome the drawbacks of the previous methods. The proposed algorithm approaches the approximate optimal solution of Transductive support vector machine optimization problem by ant colony optimization algorithm, and the advantage of transductive learning can be fully demonstrated. Experiments on several UCI standard datasets and the newsgroups 20 dataset showed that, with respect to running time and classification precision, the proposed algorithm has obvious advantage over the previous algorithms.
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Acknowledgments
This work is sponsored by the National Natural Science Foundation of China (Nos. 61402246, 61402126, 61370083, 61370086, 61303193, and 61572268), a Project of Shandong Province Higher Educational Science and Technology Program (No. J15LN38, J14LN31), Qingdao indigenous innovation program (No. 15-9-1-47-jch), the Project of Shandong Provincial Natural Science Foundation of China (No. ZR2014FL019), the Open Project of Collaborative Innovation Center of Green Tyres & Rubber (No. 2014GTR0020), the National Research Foundation for the Doctoral Program of Higher Education of China (No. 20122304110012), the Science and Technology Research Project Foundation of Heilongjiang Province Education Department (No. 12531105), Heilongjiang Province Postdoctoral Research Start Foundation (No. LBH-Q13092), and the National Key Technology R&D Program of the Ministry of Science and Technology under Grant No. 2012BAH81F02.
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Yu, X., Ren, Cn., Zhou, Yp., Wang, Y. (2016). A Transductive Support Vector Machine Algorithm Based on Ant Colony Optimization. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_13
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DOI: https://doi.org/10.1007/978-981-10-2053-7_13
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