Chih-Jen Lin's recent papers (full list and BibTeX file)

• G.-X. Yuan, K.-W. Chang, C.-J. Hsieh, and C.-J. Lin. A comparison of optimization methods for large-scale L1-regularized linear classification , 2009.

• Y.-W. Chang, C.-J. Hsieh, K.-W. Chang, M. Ringgaard, and C.-J. Lin. Low-degree polynomial mapping of data for SVM, 2009.

• F.-L. Huang, C.-J. Hsieh, K.-W. Chang, and C.-J. Lin. Iterative scaling and coordinate descent methods for maximum entropy models, 2009. A brief version appears as a short paper at ACL 2009.

• R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A library for large linear classification . Journal of Machine Learning Research 9(2008), 1871-1874. Note that we include some implementation details in the appendices of this paper.

• Y.-W. Chang and C.-J. Lin. Feature Ranking Using Linear SVM . JMLR Workshop and Conference Proceedings: Causation and Prediction Challenge (WCCI 2008) 3(2008), 53-64. Code.

• Y. Song, W.-Y. Chen, H. Bai, C.-J. Lin, and E. Y. Chang. Parallel spectral clustering . ECML/PKDD 2008. Code

• S. S. Keerthi, S. Sundararajan. K.-W. Chang, C.-J. Hsieh, and C.-J. Lin, A sequential dual method for large scale multi-class linear SVMs . KDD 2008.

• C.-J. Hsieh K.-W. Chang, C.-J. Lin, S. S. Keerthi, and S. Sundararajan. A Dual Coordinate Descent Method For Large-Scale Linear SVM. ICML 2008.

• K.-W. Chang, C.-J. Hsieh, and C.-J. Lin. Coordinate Descent Method for Large-scale L2-loss Linear SVM. Journal of Machine Learning Research 9(2008), 1369-1398.

• R.-E. Fan and C.-J. Lin. A Study on Threshold Selection for Multi-label Classification , 2007.

• C.-J. Lin, R. C. Weng, and S. S. Keerthi. Trust region Newton method for large-scale logistic regression. Journal of Machine Learning Research 9(2008), 627-650. A short version appears in ICML 2007.
  Software available at liblinear.

• L. Bottou and C.-J. Lin. Support Vector Machine Solvers. In Large Scale Kernel Machines, Léon Bottou, Olivier Chapelle, Dennis DeCoste, and Jason Weston editors, 1-28, MIT Press, Cambridge, MA., 2007.

• T.-K. Huang, C.-J. Lin, and R. C. Weng. Ranking Individuals by group comparisons. Journal of Machine Learning Research, 9(2008), 2187-2216. A short version appears in ICML 2006. Download data used in this work.

• C.-J. Lin. On the Convergence of Multiplicative Update Algorithms for Non-negative Matrix Factorization. IEEE Transactions on Neural Networks, 18(2007), 1589-1596.

• C.-J. Lin. Projected gradient methods for non-negative matrix factorization. Neural Computation, 19(2007), 2756-2779.

• R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working Set Selection Using Second Order Information for Training SVM. Journal of Machine Learning Research, 6(2005), 1889-1918.

• P.-H. Chen, R.-E. Fan, and C.-J. Lin. A Study on SMO-type Decomposition Methods for Support Vector Machines . IEEE Transactions on Neural Networks, 17(2006), 893-908.

• Y.-W. Chen and C.-J. Lin, Combining SVMs with various feature selection strategies. in the book "Feature extraction, foundations and applications." Springer, 2006.

• T.-K. Huang, R. C. Weng, and C.-J. Lin. Generalized Bradley-Terry Models and Multi-class Probability Estimates. Journal of Machine Learning Research, 7(2006), 85-115. A (very) short version of this paper appears in NIPS 2004.

• T.-F. Wu, C.-J. Lin, and R. C. Weng. Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research, 5(2004), 975-1005. A short version appears in NIPS 2003.

• C.-W. Hsu, C.-C. Chang, C.-J. Lin. A practical guide to support vector classification . Technical report, Department of Computer Science, National Taiwan University. July, 2003.

• M.-W. Chang and C.-J. Lin. Leave-one-out Bounds for Support Vector Regression Model Selection. Neural Computation, 17(2005), 1188-1222.

• H.-T. Lin, C.-J. Lin, and R. C. Weng. A note on Platt's probabilistic outputs for support vector machines. Machine Learning, 68(2007), 267-276.

• P.-H. Chen, C.-J. Lin, and B. Schölkopf. A tutorial on nu-support vector machines. Applied Stochastic Models in Business and Industry , 21(2005), 111-136.

• 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. March 2003.

• W.-C. Kao, K.-M. Chung, T. Sun, and C.-J. Lin. Decomposition Methods for Linear Support Vector Machines. Neural Computation, 16(2004), 1689-1704.

• B.-J. Chen, M.-W. Chang, and C.-J. Lin. Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001. IEEE Transactions on Power Systems. 19(2004), 1821-1830.

• K.-M. Chung, W.-C. Kao, C.-L. Sun, L.-L. Wang, and C.-J. Lin. Radius Margin Bounds for Support Vector Machines with the RBF Kernel . Neural Computation, 15(2003), 2643-2681.

• S. S. Keerthi and C.-J. Lin. Asymptotic behaviors of support vector machines with Gaussian kernel . Neural Computation, 15(2003), 1667-1689.

• M.-W. Chang, C.-J. Lin, and R. C. Weng. Analysis of nonstationary time series using support vector machines April 2002.

• K.-M. Lin and C.-J. Lin A study on reduced support vector machines. IEEE Transactions on Neural Networks, 14(2003), 1449-1559.

• M.-W. Chang, C.-J. Lin, and R. C. Weng. Analysis of switching dynamics with competing support vector machines Proceedings of IJCNN, May 2002.
  Extended version is here. IEEE Transactions on Neural Networks, 15(2004), 720-727.

• M.-W. Chang, B.-J. Chen and C.-J. Lin. EUNITE Network Competition: Electricity Load Forecasting , November 2001. Winner of EUNITE world wide competition on electricity load prediction.

• C.-J. Lin. Linear convergence of a decomposition method for support vector machines , November 2001.

• C.-J. Lin. Asymptotic convergence of an SMO algorithm without any assumptions . IEEE Transactions on Neural Networks 13(2002), 248-250.

• C.-C. Chang and C.-J. Lin. IJCNN 2001 Challenge: Generalization Ability and Text Decoding , Proceedings of IJCNN, July 2001. Winner of IJCNN Challenge.

• C.-J. Lin. A Formal Analysis of Stopping Criteria of Decomposition Methods for Support Vector Machines , IEEE Transactions on Neural Networks, 13(2002), 1045-1052.

• C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector machines , IEEE Transactions on Neural Networks, 13(2002), 415-425.

• C.-C. Chang and C.-J. Lin. Training nu-support vector regression: theory and algorithms , Neural Computation, 14(2002), 1959-1977. Implementation available in libsvm .

• S.-P. Liao, H.-T. Lin, and C.-J. Lin. A note on the decomposition methods for support vector regression . Neural Computation, 14(2002), 1267-1281.

• J.-H. Lee and C.-J. Lin. Automatic model selection for support vector machines , November 2000. Implementation available in looms .
• C.-J. Lin. On the convergence of the decomposition method for support vector machines , IEEE Transactions on Neural Networks 12(2001), 1288-1298.
• C.-W. Hsu and C.-J. Lin. A simple decomposition method for support vector machines , Machine Learning 46(2002), 291-314. Implementation available in bsvm .
• C.-C. Chang and C.-J. Lin. Training nu-Support Vector Classifiers: Theory and Algorithms , Neural Computation 13(9), 2001, 2119-2147. Implementation available in libsvm .

• C.-J. Lin. Formulations of support vector machines: a note from an optimization point of view . Neural Computation 13(2) 2001, 307-317.

• C.-C. Chang, C.-W. Hsu, and C.-J. Lin. The analysis of decomposition methods for support vector machines. in Proceeding of the Workshop on Support Vector Machines, Sixteenth International Joint Conference on Artificial Intelligence (IJCAI 99).
  Extended version appears in IEEE Transactions on Neural Networks, 11(2000), 1003-1008.
• C.-J. Lin, and R. Saigal. An incomplete Cholesky factorization for dense matrices. BIT, 40(2000), 536-558.
• C.-J. Lin, and J. J. More' . Newton's method for large bound-constrained optimization problems. SIAM Journal on Optimization, 9(1999), 1100-1127.
• S.-Y. Wu, S.-C. Fang and C.-J. Lin, Solving the General Capacity Problem. Annals of Operations Research. 103(2001), 193-211.
• S.-C. Fang, C.-J. Lin, and S.-Y. Wu Relaxations of the cutting plane method for quadratic semi-infinite programming. Journal of Computational and Applied Mathematics , 129(2001), 89-104.
• C.-J. Lin, and J. J. More' . Incomplete Cholesky Factorizations with Limited Memory. SIAM Journal on Scientific Computing, 21(1999), 24-45.
• C.-J. Lin, Preconditioning Dense Linear Systems from Large-Scale Semidefinite Programming Problems. In Proceeding of the Fifth Copper Mountain Conference on Iterative Methods. 1998. Second prize of the student paper competition.
• S.-C. Fang, S.-Y. Wu and C.-J. Lin, A relaxed cutting plane method for solving linear semi-infinite programming problems. Journal of Optimization Theory and Applications, 99(1998), 759--779.
• C.-J. Lin, S.-C. Fang , and S.-Y. Wu. An Unconstrained Convex Programming Approach for Solving Linear Semi-Infinite Programming Problems  , SIAM Journal on Optimization 8, 1998, 443--456. 

Papers before 1998 are not listed.