Paper presentation


You can choose one lead paper from the following list of papers for your term paper. Please consult background papers/related papers to create a self contained and complete writeup. Each term paper has to be presented. A schedule for this will be put up once everyone has selected a topic. Tentatively, this will in the last two weeks of the semester - on weekends.
Each registered student must do this on his/her own. No groups.
You are welcome to choose a topic/lead paper outside the given list. But please get my concurrence before you finalize.
Your topic should be chosen by 10 Feb. 2006.
Your written termpapers (soft copy - ps or pdf) should be uploaded on hk.cse.iitk.ac.in by 17 Apr. 2006. You will get a mail close to the due date giving login id/passwd information.
The papers are largely from recent issues of JMLR.

Those in red have been taken



  1. Hyunsoo Kim, Peg Howland, Haesun Park, Dimension Reduction in Text Classification with Support Vector Machines, JMLR,6,37-53,2005.
  2. Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung, Core Vector Machines: Fast SVM Training on Very Large Data Sets, JMLR,6,363-392,2005.
  3. Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil, Learning Multiple Tasks with Kernel Methods, JMLR,6,615-637,2005
  4. Fabio Aiolli, Alessandro Sperduti, Multiclass Classification with Multi-Prototype Support Vector Machines, JMLR,6,817-850,2005.
  5. Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson, Learning the Kernel with Hyperkernels, JMLR,6,1043-1071,2005.
  6. Charles A. Micchelli, Massimiliano Pontil, Learning the Kernel Function via Regularization, JMLR,6,1099-1125,2005.
  7. Alain Rakotomamonjy, Stephane Canu, Frames, Reproducing Kernels, Regularization and Learning, JMLR,6,1485-1515,2005.
  8. Antoine Bordes, Seyda Ertekin, Jason Weston, Leon Bottou, Fast Kernel Classifiers with Online and Active Learning, JMLR,6,1579-1619,2005.
  9. Nicolo Cesa-Bianchi, Claudio Gentile, Luca Zaniboni, Incremental Algorithms for Hierarchical Classification, JMLR,7,31-54,2006.
  10. Corinna Cortes, Patrick Haffner, Mehryar Mohri, Rational Kernels: Theory and Algorithms, JMLR,5,1035-1062,2004.
  11. Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto, Michele Piana, Alessandro Verri, Some Properties of Regularized Kernel Methods, JMLR,5,1363-1390,2004.
  12. Christina Leslie, Rui Kuang, Fast String Kernels using Inexact Matching for Protein Sequences, JMLR,5,1435-1455,2004.
  13. Di-Rong Chen, Qiang Wu, Yiming Ying, Ding-Xuan Zhou, Support Vector Machine Soft Margin Classifiers: Error Analysis, JMLR,5,1143-1175,2004.
  14. Gert R.G. Lanckriet, Nello Cristianini, Peter Bartlett, Laurent El Ghaoui, Michael I. Jordan, Learning the Kernel Matrix with Semidefinite Programming, JMLR,5,27-72,2004.
  15. Ryan Rifkin, Aldebaro Klautau, In Defense of One-Vs-All Classification, JMLR,5,101-141,2004.
  16. Kenji Fukumizu, Francis R. Bach, Michael I. Jordan, Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces, JMLR,5,73-99,2004.
  17. N. Cristianini, J. Kandola, A. Elisseeff, and J. Shawe-Taylor, On Kernel Target Alignment, Paper on www.support-vector.net.
  18. G. R. G. Lanckriet, N. Cristianini, M. I. Jordan, and W. S. Noble, Kernel-based integration of genomic data using semidefinite programming, Chapter in Kernel Methods in Computational Biology, MIT Press, 2004.
  19. Brian Kulis, Sugato Basu, Inderjit Dhillon and Raymond Mooney, Semi-supervised graph clustering: a kernel approach ICML, 2005.
  20. Hisashi Kashima, Yuta Tsuboi, Semi-supervised graph clustering: a kernel approach, ICML,69, p58, 2004.
  21. Taishin Kin, Tsuyoshi Kato, Koji Tsuda, in Kernel methods in Comp. Biology, MIT Press, 2004.
  22. Alex Holub, Max Welling, Pietro Perona, Combining generative models and Fisher kernels for object recognition, Proc. 10th ICCV, 2005.


Schedule(will be put up soon)

No.WhoTopic Presentation (HRS)
1.Zahir Koradia Dimension reduction in Text Classification with SVM 12/4/06 - class
2.Deepak Kumar Kernel based integration of genomic data using SDP 13/4/06 - class
3.Amar Kamat Incremental alg. for hierarchical classification 15/4/06, 9:00hrs
4.Sumit Mundhra Learning the kernel with hyperkernels 15/4/06,10:00hrs
5.Arun Iyer Semi-supervised graph clustering: a kernel approach 15/4/06, 11:00hrs
6.Shubhransu Maji Fast String Kernels using Inexact Matching for Protein Sequences 15/4/06, 12:00hrs
7.Neha Sugandh Learning the kernel matrix with SDP 15/4/06, 14:30hrs
8.Shashi Mittal Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs 15/4/06, 15:30hrs
9.Sumeet Agarwal Combining generative models and Fisher kernels for object recognition 15/4/06, 16:30hrs
10.Pankaj Baranwal Protein classification via kernel matrix completion 15/4/06, 17:30hrs
11.Rakesh Kumar Frames, reproducing kernels, regularization and learning, 16/4/06, 9:00hrs
12.Ankit Soni Domain Kernels for Word Sense Disambiguation 16/4/06, 10:00hrs
13.Navdeep Bhulli Learning Multiple Tasks with Kernel Methods 16/4/06, 11:00hrs
14.Pradeep Sonkar Core vector machines: fast SVM training on very large data sets 16/4/06, 12:00hrs
15.Rahul Srivastava Multiclass Classification with Multi-Prototype Support Vector Machines 19/4/06, class
16.Ankur Mittal 20/4/06, class