A New Classification Method Based on Semi-supervised Support Vector Machine

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• 作者：Weijin Jiang (18)
  Yao Lina (19)
  Jiang Xinjun (19)
  Xu Yuhui (18)
  
• 关键词：Classification algorithm ; Smooth ; Spline unction ; Semi ; supervised support vector machine
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8944
• 期：1
• 页码：633-645
• 全文大小：283 KB
• 参考文献：1. Deng, N.Y., Tian, Y.J.: New Method in Data Mining: Support Vector Machine. Science Press, Beijing (2004)
  2. Miller, D.J., Uyar, H.S.: A mixture of experts classifier with learning based on both labelled and unlabelled data. In: Proc. NIPS 9, pp. 571鈥?77 (1997)
  3. Chapelle, O., Sindhwani, V., Keerthi, S.S.: Optimization Techniques for Semi - Supervised Support Vector Machines. Journal of Machine Learning Research 9, 203鈥?33 (2008)
  4. Sun, L., Jing, L., Xia, X.: A new proximal support vector machine for semi-supervised classification. In: Wang, J., Yi, Z., 呕urada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 1076鈥?082. Springer, Heidelberg (2006) CrossRef
  5. Reddy, S., Shevade, S., Murty, M.N.: A Fast Quasi-Newton Method for Semi-Supervised SVM. Pattern Recognition 44, 2305鈥?313 (2011) CrossRef
  6. Angelini, L., Marinazzo, D., Pellicoro, M., Stramaglia, S.: Semi-supervised Learning by Search of Optimal Target Vector. Pattern Recognition Letters 29, 34鈥?9 (2008) CrossRef
  7. Kalakech, M., Biela, P., Macaire, L., Hamad, D.: Constraint Scores for Semi-supervised Feature Selection: A comparative study. Pattern Recognition Letters 32, 656鈥?65 (2011) CrossRef
  8. Liu, Y.Q., Liu, S.Y., Gu, M.T.: Polynomial Smooth Classification Algorithm of Semi-supervised Support Vector Machines. Computer Science 36(7), 179鈥?81 (2009)
  9. Astorino, A., Fuduli, A.: Nonsmooth optimization techniques for semi-supervised classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(12), 2135鈥?142 (2007) CrossRef
  10. Liao, D., Jiang, B., Wei, X., Li, X., Zhuang, Z.: Fast learning algorithm with progressive transductive support vector machine. Systems Engineering and Electronics 29, 87鈥?1 (2007)
  11. Chaplle, O., Zien, A.: Semi-supervised classification by low density separation. In: The 10th International Workshop on Artificial Intelligence and Statistics, Barbados (2005)
  12. Zhu, X., Ghahramani, Z., Lafferty, J.: Semisupervised learning using gaussian Fields and harmonic functions. In: Proc. 20th Int. Conf. on Mach. Learn., pp. 912鈥?19 (2003)
  13. Lee, Y.J., Mangasarian, O.L.: SSVM: A Smooth Support Vector Machine for Classification. Computational Optimization and Applications 22(1), 5鈥?2 (2001) CrossRef
  14. Yuan, Y.B., Yan, J., Xu, C.X.: Polynomial Smooth Support Vector Machine. Chinese Journal of Computers 28(1), 9鈥?7 (2005)
  15. Musicant, D.R., Feinberg, A.: Active set support vector regression. IEEE Trans. Neural Net-works 15(2), 268鈥?75 (2004) CrossRef
  16. Park, S., Zhang, B.: Co-trained support vector machines for large scale unstructured document classification using unlabeled data and syntactic information. Inform. Process. Manag. 40(3), 421鈥?39 (2004) CrossRef
  17. Wu, J.: Support Vector Machines learning Algorithm Research Based on Optimization Theory. Xian, University of Electronic Science and Technology Xian, Applied Mathematics (2009)
  18. Zhang, X.D., Shao, S., Liu, Q.S.: Smooth support vecter machine model based on spline function. Jornal of University of Science and Tecnology Beijing 34(6), 718鈥?25 (2012)
  19. Xiong, J.Z., Yuan, H.Q., Peng, H.: A General Formulation of Polynomial Smooth Support Vector Machines. Journal of Computer Research and Development 45(8), 1346鈥?353 (2008)
  
• 作者单位：Weijin Jiang (18)
  Yao Lina (19)
  Jiang Xinjun (19)
  Xu Yuhui (18)
  
  18. School of Computer and Information Engineering, Hunan University of Commerce, Changsha, China
  19. Department of Computer, Hunan Radio and TV University, South of Furong Road, Changsha, 410005, Hunan, China
  
• 丛书名：Human Centered Computing
• ISBN：978-3-319-15554-8
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

Semi-supervised learning using tag vector machine is a relatively new method of data classification and label-free. Semi-supervised support vector machines model the objective function is not smooth and fast optimization algorithm to solve the model cannot be applied. This paper presents a general three-moment method 3 times differentiable at the origin of construct quintic spline functions, construction of hinge can be used to approximate symmetry loss functions, the approximate accuracy estimation of and quintic spline functions. And on top of this, deduced five and a half times b-spline smoothing support vector machines for non-smooth a-smoothing model analyses the convergence. Broyden-Fletcher-Goldfarb-Shanno (storage) algorithm can be used in new models. Experimental results show that the new model has a better performance.