Journal Papers

• Hsuan-Tien Lin and Ling Li. Support Vector Machinery for Infinite Ensemble Learning. Journal of Machine Learning Research, 9(2), 285-312, 2008. (Official Version).
• Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng. A Note on Platt's Probabilistic Outputs for Support Vector Machines. Machine Learning, 68(3), 267-276, 2007. (Official Version).
• Shuo-Peng Liao, Hsuan-Tien Lin, and Chih-Jen Lin. A Note on the Decomposition Methods for Support Vector Regression. Neural Computation, 14(6), 1267-1281, 2002. (a shorter version appeared in IJCNN '01). (Official Version).

Peer-Reviewed Conference Papers

• Ling Li and Hsuan-Tien Lin. Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent. In Proceedings of IJCNN '07, 749-754, IEEE, 2007. (Talk).
• Ling Li and Hsuan-Tien Lin. Ordinal Regression by Extended Binary Classification. In B. Schölkopf et al., eds., Advances in Neural Information Processing Systems: Proceedings of the 2006 Conference (NIPS '06), 865-872, MIT Press, 2007. (Spotlight), (Poster).
• Hsuan-Tien Lin and Ling Li. Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice. In J. Balcazár et al., eds., Algorithmic Learning Theory: ALT '06, vol. 4264 of Lecture Notes in Artificial Intelligence, 319-333, Springer-Verlag, 2006. (Talk),
• Hsuan-Tien Lin and Ling Li. Novel Distance-Based SVM Kernels for Infinite Ensemble Learning. In Proceedings of ICONIP '05, 761-766, 2005. (Talk).
• Hsuan-Tien Lin and Ling Li. Infinite Ensemble Learning with Support Vector Machines. In J. Gama et al., eds., Machine Learning: ECML '05, vol. 3720 of Lecture Notes in Artificial Intelligence, 242-254, Springer-Verlag, 2005. (Talk).
• Ling Li, Amrit Pratap, Hsuan-Tien Lin and Yaser S. Abu-Mostafa. Improving Generalization by Data Categorization. In A. Jorge et al., eds., Knowledge Discovery in Databases: PKDD '05, vol. 3721 of Lecture Notes in Artificial Intelligence, 157-168, Springer-Verlag, 2005. (Code).
• Shuo-Peng Liao, Hsuan-Tien Lin, and Chih-Jen Lin. A Note on the Decomposition Methods for Support Vector Regression. In Proceedings of IJCNN '01, 1474-1479, IEEE/Omnipress, 2001.

Peer-Reviewed Workshop Papers

• Hung-Yi Lo, Kai-Wei Chang, Shang-Tse Chen, Tsung-Hsien Chiang, Chun-Sung Ferng, Cho-Jui Hsieh, Yi-Kuang Ko, Tsung-Ting Kuo, Hung-Che Lai, Ken-Yi Lin, Chia-Hsuan Wang, Hsiang-Fu Yu, Chih-Jen Lin, Hsuan-Tien Lin and Shou-de Lin. An Ensemble of Three Classifiers for KDD Cup 2009: Expanded Linear Model, Heterogeneous Boosting, and Selective Naïve Bayes. KDDCup '09, 2009. To appear in JMLR Proceedings. (Third Place of the Slow Track).
• Hsuan-Tien Lin and Ling Li. Combining Ordinal Preferences by Boosting. Preference Learning Workshop @ ECML/PKDD '09, 2009. (Talk).
• Hsuan-Tien Lin and Ling Li. Analysis of SAGE Results with Combined Learning Techniques. In P. Berka and B. Crémilleux, eds., Proceedings of the ECML/PKDD 2005 Discovery Challenge, 102-113, 2005. (Talk).

Thesis

• Hsuan-Tien Lin. From Ordinal Ranking to Binary Classification. Ph.D. Dissertation, California Institute of Technology, May 2008. (Caltech ETD), (Local PDF)
• Hsuan-Tien Lin. Infinite Ensemble Learning with Support Vector Machines. Master's Thesis, California Institute of Technology, May 2005. (Caltech ETD), (Updated PDF).

Technical Report

• Hsuan-Tien Lin and Ling Li. Reduction from Ordinal Ranking to Binary Classification. National Taiwan University, September 2009 (NTUCLL.TR001). (some preliminary parts appeared in ALT '06 and NIPS '06).

• Hsuan-Tien Lin and Chih-Jen Lin. A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods. National Taiwan University, March 2003.

Last updated at CST 15:32, November 10, 2009 Please feel free to contact me: