An efficient method for learning nonlinear ranking SVM functions

Information Sciences

Volumn 209, Issue , 2012, Pages 37-48

  Yu, Hwanjo ^a   Kim, Jinha ^a   Kim, Youngdae ^a   Hwang, Seungwon ^a   Lee, Young Ho ^b  

^a Pohang University of Science and Technology (POSTECH)   (South Korea)

^b Gachon University College of Medicine   (South Korea)

Author keywords

Rank learning; RankSVM

Indexed keywords

DATA RETRIEVAL; DIFFERENCE VECTORS; EXPERIMENTAL COMPARISON; FAST LEARNING; LINEAR KERNEL; NONLINEAR KERNELS; RANK LEARNING; RANKING SVM; RANKSVM; SUPPORT VECTOR;

SEARCH ENGINES;

LEARNING SYSTEMS;

EID: 84862653947     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2012.03.022     Document Type: Article

References (44)

1. R.A. Baeza-Yates, and B. Ribeiro-Neto Modern Information Retrieval 1999 Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA
2. C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender Learning to rank using gradient descent Proceedings of the 22nd international conference on Machine learning ICML '05 2005 ACM New York, NY, USA 89 96 (Pubitemid 43183320)
3. C.J.C. Burges A tutorial on support vector machines for pattern recognition Data Min. Knowl. Discov. 2 1998 121 167 (Pubitemid 128695475)
4. B. Cao, D. Shen, J.T. Sun, Q. Yang, and Z. Chen Feature selection in a kernel space Proceedings of the 24th International Conference on Machine Learning ICML '07 2007 ACM New York, NY, USA 121 128
5. Y. Cao, J. Xu, T.Y. Liu, H. Li, Y. Huang, and H.W. Hon Adapting ranking SVM to document retrieval Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR '06 2006 ACM New York, NY, USA 186 193
6. B.H. Cho, H. Yu, J. Lee, Y.J. Chee, I.Y. Kim, and S. Kim Nonlinear support vector machine visualization for risk factor analysis using nomograms and localized radial basis function kernels Inform. Technol. Biomed. IEEE Trans. 12 2008 247 256 (Pubitemid 351460492)
7. N. Christianini, and J. Shawe-Taylor An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods 2000 Cambridge University Press
8. Y. Freund, R. Iyer, R.E. Schapire, and Y. Singer An efficient boosting algorithm for combining preferences J. Mach. Learn. Res. 4 2003 933 969
9. G.M. Fung, and O.L. Mangasarian A feature selection Newton method for support vector machine classification Comput. Optim. Appl. 28 2004 185 202
10. I. Guyon, and A. Elisseeff An introduction to variable and feature selection J. Mach. Learn. Res. 3 2003 1157 1182
11. Q. He, and C. Wu Separating theorem of samples in banach space for support vector machine learning Int. J. Mach. Learn. Cybern. 2 2011 49 54
12. R. Herbrich, T. Graepel, and K. Obermayer Large Margin Rank Boundaries for Ordinal Regression Large Margin Rank Boundaries for Ordinal Regression 2000 MIT Press Cambridge, MA
13. I.A. Illán, J.M. Górriz, J. Ramírez, D. Salas-Gonzalez, M.M. López, F. Segovia, R. Chaves, M. Gómez-Rio, and C.G. Puntonet 18f-fdg pet imaging analysis for computer aided alzheimer's diagnosis Inform. Sci. 181 2011 903 916
14. X. Jiang, H. Yu, SVM-JAVA: A Java Implementation of the SMO (Sequential Minimal Optimization) for Training SVM, < http://dm.hwanjoyu.org/svm-java >.
15. T. Joachims, SVM-light, < Http://svmlight.joachims.org/ >.
16. T. Joachims Optimizing search engines using clickthrough data Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD '02 2002 ACM New York, NY, USA 133 142
17. T. Joachims Training linear SVMS in linear time Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD '06 2006 ACM New York, NY, USA 217 226 (Pubitemid 44535518)
18. Y.J. Lee, and O.L. Mangasarian RSVM: Reduced support vector machines Proceedings of the SIAM International Conference on Data Mining 2001 SIAM Philadelphia 00 07
19. Z. Liu, Q. Wu, Y. Zhang, and C. Philip Chen Adaptive least squares support vector machines filter for hand tremor canceling in microsurgery Int. J. Mach. Learn. Cybern. 2 2011 37 47 doi:10.1007/s13042-011-0012-5
20. S. Maldonado, R. Weber, and J. Basak Simultaneous feature selection and classification using kernel-penalized support vector machines Inform. Sci. 181 2011 115 128
21. O.L. Mangasarian Generalized Support Vector Machines 2000 MIT Press
22. O.L. Mangasarian Exact 1-norm support vector machines via unconstrained convex differentiable minimization J. Mach. Learn. Res. 7 2006 1517 1530 (Pubitemid 44373695)
23. O.L. Mangasarian, E.W. Wild, Feature Selection for Nonlinear Kernel Support Vector Machines, Technical Report, University of Wisconsin, Madison, 1998.
24. J. Platt Fast training of support vector machines using sequential minimal optimization A.S.B. Scholkopf, C. Burges, Advances in Kernel Methods: Support Vector Machines 1998 MIT Press Cambridge, MA
25. T. Qin, T.Y. Liu, Letor: Learning to Rank for Information Retrieval, < http://research.microsoft.com/users/LETOR/ >.
26. T. Qin, X.D. Zhang, D.S. Wang, T.Y. Liu, W. Lai, and H. Li Ranking with multiple hyperplanes Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR '07 2007 ACM New York, NY, USA 279 286 (Pubitemid 350164972)
27. F. Radlinski, and T. Joachims Query chains: learning to rank from implicit feedback Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining KDD '05 2005 ACM New York, NY, USA 239 248 (Pubitemid 43218285)
28. J. Ramírez, J.M. Górriz, D. Salas-Gonzalez, A. Romero, M. López, I. Álvarez, M. Gómez-Río, Computer-aided diagnosis of alzheimer's type dementia combining support vector machines and discriminant set of features, Inform. Sci. (2009).
29. B. Schölkopf, R. Herbrich, and A.J. Smola A generalized representer theorem Proceedings of the 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computational Learning Theory COLT '01/EuroCOLT '01 2001 Springer-Verlag London, UK 416 426 (Pubitemid 33312837)
30. V. Vapnik Statistical Learning Theory 1998 John Wiley and Sons
31. X. Wang, L. Dong, and J. Yan Maximum ambiguity based sample selection in fuzzy decision tree induction Knowl. Data Eng. IEEE Trans. PP 2011 1
32. X. Wang, S. Lu, and J. Zhai Fast fuzzy multi-category SVM based on support vector domain description Int. J. Pattern Recogn. Artif. Intell. 22 1 2008 18 31
33. X.Z. Wang, and C.R. Dong Improving generalization of fuzzy if-then rules by maximizing fuzzy entropy Fuzzy Syst. IEEE Trans. 17 2009 556 567
34. X.Z. Wang, J.H. Zhai, and S.X. Lu Induction of multiple fuzzy decision trees based on rough set technique Inform. Sci. 178 2008 3188 3202
35. J. Xu, and H. Li Adarank: a boosting algorithm for information retrieval Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR '07 2007 ACM New York, NY, USA 391 398 (Pubitemid 350164985)
36. H. Yu Svm selective sampling for ranking with application to data retrieval Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining KDD '05 2005 ACM New York, NY, USA 354 363 (Pubitemid 43218297)
37. H. Yu, S.w. Hwang, and K.C.C. Chang Enabling soft queries for data retrieval Inform. Syst. 32 2007 560 574 (Pubitemid 46240628)
38. H. Yu, Y. Kim, S.W. Hwang, RV-SVM: An Efficient Method for Learning Ranking SVM, Technical Report, Department of Computer Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Korea, 2008 < http://iis.hwanjoyu.org/rv-svm >.
39. H. Yu, I. Ko, Y. Kim, S. Hwang, and W.S. Han Exact indexing for support vector machines Proceedings of the 2011 International Conference on Management of Data SIGMOD '11 2011 ACM New York, NY, USA 709 720
40. H. Yu, J. Yang, W. Wang, J. Han, Discovering compact and highly discriminative features or feature combinations of drug activities using support vector machines, in: IEEE Computer Society Bioinformatics Conf. (CSB'03), pp. 220-228.
41. J. Zhang, and Y. Wang A rough margin based support vector machine Inform. Sci. 178 2008 2204 2214
42. S. Zhang, P. McCullagh, C. Nugent, H. Zheng, and M. Baumgarten Optimal model selection for posture recognition in home-based healthcare Int. J. Mach. Learn. Cybern. 2 2011 1 14
43. S.M. Zhou, J.Q. Gan, and F. Sepulveda Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface Inform. Sci. 178 2008 1629 1640
44. J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani 1-Norm support vector machines S. Thrun, L. Saul, B. Schölkopf, Advances in Neural Information Processing Systems vol. 16 2004 MIT Press Cambridge, MA