Journal Papers

• H.-T. Lin and L. Li. Support Vector Machinery for Infinite Ensemble Learning. Journal of Machine Learning Research, 9(2), 285-312, 2008. (Official Version).
• H.-T. Lin, C.-J. Lin, and R. C. Weng. A Note on Platt's Probabilistic Outputs for Support Vector Machines. Machine Learning, 68(3), 267-276, 2007. (Code), (Official Version).
• S.-P. Liao, H.-T. Lin, and C.-J. 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 and Workshop Papers

• L. Li and H.-T. Lin. Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent. In Proceedings of IJCNN '07, 749-754, IEEE, 2007. (Talk).
• L. Li and H.-T. 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. (Code), (Spotlight), (Poster).
• H.-T. Lin and L. 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), (Code).
• H.-T. Lin and L. Li. Novel Distance-Based SVM Kernels for Infinite Ensemble Learning. In Proceedings of ICONIP '05, 761-766, 2005. (Talk), (Code).
• H.-T. Lin and L. 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).
• H.-T. Lin and L. 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), (Code).
• L. Li, A. Pratap, H.-T. Lin and Y. 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).
• S.-P. Liao, H.-T. Lin, and C.-J. Lin. A Note on the Decomposition Methods for Support Vector Regression. In Proceedings of IJCNN '01, 1474-1479, IEEE/Omnipress, 2001.

Thesis

• H.-T. Lin. Infinite Ensemble Learning with Support Vector Machines. Master's Thesis, California Institute of Technology, May 2005. (Caltech ETD), (Updated PDF), (Code).

Technical Report

• H.-T. Lin and C.-J. Lin. A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods. National Taiwan University, March 2003. (under journal review).

Feel free to contact me: "htlin" at "csie.ntu.edu.tw"