Learning Systems Group

Publications

For full publications, please see individual pages of Yaser Abu-Mostafa, Ling Li, Hsuan-Tien Lin, Amrit Pratap. For publications older than 2003, see this page.

2008

• H.-T. Lin and L. Li. Support Vector Machinery for Infinite Ensemble Learning. Journal of Machine Learning Research, 9(2): 285-312, 2008.

2007

• L. Li and H.-T. Lin. Optimizing 0/1 Loss for Perceptrons by Random Coordinate Descent. In IJCNN 2007: the 20th International Joint Conference on Neural Networks, 2007.

2006

• L. Li and H.-T. Lin. Ordinal Regression by Extended Binary Classification. In NIPS 2006: the 20th Annual Conference on Neural Information Processing Systems, 2006.
• H.-T. Lin and L. Li. Large-Margin Thresholded Ensembles for Ordinal Regression: Theory and Practice. In ALT 2006: Proceedings of the 17th International Conference on Algorithmic Learning Theory, 2006.
• L. Li. Multiclass Boosting with Repartitioning. In ICML 2006: Proceedings of the 23rd International Conference on Machine Learning, 2006.
• L. Li. Data Complexity in Machine Learning and Novel Classification Algorithms. Ph.D. Thesis, California Institute of Technology, May 2006.
• L. Li and Y. S. Abu-Mostafa. Data Complexity in Machine Learning. Computer Science Technical Report CaltechCSTR:2006.004, California Institute of Technology, May 2006.

2005

• H.-T. Lin and L. Li. Novel Distance-Based SVM Kernels for Infinite Ensemble Learning. ICONIP 2005, pp. 761-766, 2005.
• 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, pp. 102-113, Porto, Portugal, 2005.
• 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 2005, vol. 3721 of Lecture Notes in Artificial Intelligence, pp. 157-168. Springer-Verlag, 2005.
• H.-T. Lin and L. Li. Infinite Ensemble Learning with Support Vector Machines. In J. Gama et al., eds., Machine Learning: ECML 2005, vol. 3720 of Lecture Notes in Artificial Intelligence, pp. 242-254. Springer-Verlag, 2005.
• L. Li. Perceptron Learning with Random Coordinate Descent. Computer Science Technical Report CaltechCSTR:2005.006, California Institute of Technology, Aug. 2005.
• A. Angelova, Y. Abu-Mostafa, and P. Perona. Pruning Training Sets for Learning of Object Categories. In Computer Vision and Pattern Recognition, 2005 (CVPR'05). Vol. 1, pp. 494-501, 2005.
• H.-T. Lin. Infinite Ensemble Learning with Support Vector Machines. M.S. Thesis, California Institute of Techology, May 2005.
  Caltech ETD Updated PDF.

2004

• L. Li, A. Martinoli, and Y. S. Abu-Mostafa. Learning and Measuring Specialization in Collaborative Swarm Systems. Adaptive Behavior, 12(3-4):199-212, 2004.
  abstract, PDF, PS, PS.GZ
• Y. S. Abu-Mostafa, X. Song, A. Nicholson, and M. Magdon-Ismail. The Bin Model. Computer Science Technical Report CaltechCSTR:2004.002, California Institute of Technology, Jul. 2004.
  abstract, PDF, PS
• A. Pratap. Maximum drawdown of a Brownian motion and AlphaBoost: a boosting algorithm M.S. Thesis, California Institute of Techology, May 2004.
• M. Magdon-Ismail, A. F. Atiya, A. Pratap and Y. S. Abu-Mostafa
  On the Maximum Drawdown of the Brownian Motion. In Journal of Applied Probability, Volume 41, Number 1, March, 2004

2003

• L. Li, A. Martinoli, and Y. S. Abu-Mostafa. Diversity and Specialization in Collaborative Swarm Systems. In C. Anderson and T. Balch, eds., Proceedings of the 2nd International Workshop on the Mathematics and Algorithms of Social Insects, pp. 91-98, Atlanta, GA, 2003.
  PDF, PS, PS.GZ
• L. Li, Y. S. Abu-Mostafa, and A. Pratap. CGBoost: Conjugate Gradient in Function Space. Computer Science Technical Report CaltechCSTR:2003.007, California Institute of Technology, Aug. 2003.
  abstract, PDF, PS, PS.GZ
• M. Magdon-Ismail, A. F. Atiya, A. Pratap and Y. S. Abu-Mostafa. The Maximum Drawdown of the Brownian Motion. In IEEE Conference on Computational Intelligence in Financial Engineering (CIFER), pp. 243-247, Hong Kong, March 2003.