My Publications
Audio
Rifkin, Schutte, Saad, Bouvrie and Glass. “Noise Robust Phonetic Classification with Linear Regularized Least Squares and Second-Order Features”, Proceedings of International Conference on Acoustics, Speech and Signal Processing 2007.
Rifkin and Mesgarani. “Discriminating Speech and Non-Speech with Regularized Least Squares”. Proceedings of the Ninth International Conference on Spoken Language Processing, Pittsburgh, 2006.
Rifkin. “Speaker Recognition Using Local Models”, Proceedings of Eurospeech 2003.
Whitman and Rifkin. “Musical Query-by-Description as a Multi-Class Learning Problem”, Proceedings of the IEEE Multimedia Signal Processing Conference (MMSP), December 2002.
The Audiomomma Music Recommendation System “The Audiomomma Music Recommendation
System.” A.I. Memo #2001-012, C.B.C.L. Memo #199, July 2001.
Moreno and Rifkin. “Using the Fisher Kernel Method for Web Audio Classification.” International Conference on Acoustics, Speech and Signal Processing 2000.
Texts and Images
Bileschi, Leung and Rifkin. “Towards Component-based Car Detection.” 2004 ECCV Workshop on Statistical Learning and Computer Vision.
Rennie and Rifkin. “Improving Multiclass Text Classification with the Support Vector Machine.” A.I. Memo #2001-026, C.B.C.L. Memo #210, October 2001.
Alvira and Rifkin. “An Empirical Comparison of SNoW and SVMs for Face Detection.” A.I. Memo #2001-004, C.B.C.L. Memo #193, January 2001.
Theory Of Learning
Rifkin and Lippert. Notes on Regularized Least Squares. MIT CSAIL Tech Report 2007-025, CBCL Memo 268.
Mansinghka, Roy, Rifkin and Tenenbaum. AClass: An online algorithm for generative classification. Proceedings of AI & Statistics 2007.
Rifkin and Lippert. Value Regularization and Fenchel Duality. Journal of Machine Learning Research, Volume 8, pp. 441-479, March 2007.
Mukherjee, Niyogi, Poggio and Rifkin. Learning Theory: Stability is Sufficient for Generalization and Necessary and Sufficient for Consistency of Empirical Risk Minimization.” Advances in Computational Mathematics, Volume 25, pp. 161-193, 2006.
Lippert and Rifkin. “Infinite-Sigma Limits for Tikhonov Regularization.” Journal of Machine Learning Research, Volume 7, pp. 855-876, May 2006.
Lippert and Rifkin. “Asymptotics of Gaussian Regularized Least Squares.” Neural Information Processing Systems 2005.
Lippert and Rifkin. Asymptoptics of Gaussian Regularized Least Squares.” MIT CSAIL Tech Report 2005-067, A.I Memo #2005-030, C.B.C.L. Memo #257.
Poggio, Rifkin, Mukherjee and Niyogi. “General Conditions for Predictivity in Learning Theory.” Nature, Vol. 428, pp. 419-422, 2004.
Rifkin and Klautau. “In Defense of One-Vs-All Classification.” Journal of Machine Learning Research, Volume 5, pp. 101-141, 2004.
Rifkin, Yeo and Poggio. “Regularized Least Squares Classification.” Advances in Learning Theory: Methods, Models and Applications, NATO Science Series III: Computer and Systems Sciences, Vol. 190, IOS Press, Amsterdam 2003. Edited by Suykens, Horvath, Basu, Micchelli, and Vandewalle.
Rifkin. “Everything Old Is New Again: A Fresh Look at Historical Approaches in Machine Learning.” PhD Thesis, MIT, 2002.
Mukherjee, Rifkin and Poggio. “Regression and Classification with Regularization.” In Nonlinear Estimation and Classification, Springer Verlag 2003. Edited by Denison, Hansen, Holmes, Mallick and Yu.
Poggio, Rifkin, Mukherjee, and Rakhlin. “Bagging Regularizes.” A.I. Memo #2002-003, C.B.C.L. Memo #214, March 2002
Poggio, Mukherjee, Rifkin, Rakhlin, and Verri “b.” Proceedings of the Conference on Uncertainty in Geometric Computations, 2001. (This version is much clearer than the A. I. Memo version.)
Poggio, Mukherjee, Rifkin, Rakhlin, and Verri. “b.” A.I. Memo #2001-011, C.B.C.L. Memo
#198, July 2001.
Rifkin, Pontil, and Verri. “A Note on Support Vector Machine Degeneracy.” A.I. Memo #1661, C.B.C.L. Memo #177, Algorithmic Learning Theory 1999. NOTE: This paper contains serious errors. The proof of Lemma 2 is bogus, and Lemma 3 and Theorem 4, which depend on Lemma 2, are false. I continue to make the paper available because it was already published when the errors were discovered (many thanks to Chih-Jen Lin), and because, as a scientist, I want my failures as well as my triumphs in public view. The second half of the paper is correct as it stands.
Pontil, Rifkin, and Evgeniou. “From Regression to Classification in Support Vector Machines.” A.I. Memo #1649, C.B.C.L. Memo #166, European Symposium on Artificial Neural Networks 1999.
Bioinformatics
Rifkin, Mukherjee, Tamayo, Ramaswamy, Yeang, Angelo, Reich, Poggio, Lander, Golub and Mesirov. “An Analytical Method for Multiclass Molecular Cancer Classification.” SIAM Review, Vol. 45, 4, pp. 706-723, 2003.
Dror, Murnick, Rinaldi, Marinescu, Rifkin and Young. “Bayesian Estimation of Transcript Levels Using a General Model of Array Measurement Noise.” Journal of Computational Biology, Vol. 10, 3, pp 433-452.
Mukherjee, Tamayo, Rogers, Rifkin, Engle, Campbell, Golub and Mesirov. “Estimating Dataset Size Requirements for Classifying DNA Microarray Data.” Journal of Computational Biology, Vol. 10, 2, pp 119-142, 2003.
Dror, Murnick, Rinaldi, Marinescu, Rifkin, and Young. “A Bayesian Approach to Transcript Estimation from Gene Array Data: The BEAM Technique.” RECOMB 2002.
Pomeroy, Tamayo, Gaasenbeek, Sturla, Angelo, Mclaughlin, Kim, Goumnerova, Black, Lau, Allen, Zagzag, Olson, Curran, Wetmore, Biegel, Poggio, Mukhrejee, Rifkin, Califano, Stolovitzky, Louis, Mesirov, Lander and Golub. “Prediction of central nervous system embryonal tumours outcome based on gene expression.” Nature, Vol. 415, 24 January 2002.
Ramaswamy, Tamayo, Rifkin, Mukherjee, Yeang, Angelo, Ladd, Reich, Latulippe, Mesirov, Poggio, Gerlad, Loda, Lander and Golub. “Multiclass cancer diagnosis using tumor gene expression signatures.” Proceedings of the National Academy of Science, vol. 98, no. 26, 18 December 2001.
Yeang, Ramaswamy, Tamayo, Mukherjee, Rifkin, Angelo, Reich, Lander, Mesirov and Golub. “Molecular Classification of Multiple Tumor Types.” Intelligent Systems in Molecular Biology, 2001.
Air Traffic Control
Ball, Hoffman, Odoni, and Rifkin. “A Stochastic Integer Program with Dual Network Structure and its Application to the Ground Holding Problem.” Accepted to Operations Research as a Technical Note, Fall 2001.
Rifkin. Masters Thesis: “The Single Airport Static Stochastic Ground Holding Problem.” February 1998. Published as NEXTOR Report T-98-1, NEXTOR Center of Excellence in Air Traffic Control.