BMRM is an open source, modular and scalable convex solver for many machine learning problems cast in the form of regularized risk minimization problem [1]. It is "modular" because the (problem-specific) loss function module is decoupled from the (regularization-specific) optimization module (e.g. quadratic programming or linear programming solvers), thus shorten the time to implement/prototype solutions to new problems. Besides, the decoupling leads to easier parallelization of the loss function computation. At the moment, BMRM can solve the following problems:
[1] | C. H. Teo, Q. Le, A. J. Smola and S. V. N. Vishwanathan, A Scalable Modular Convex Solver for Regularized Risk Minimization, KDD, 2007. [pdf] |
[2] | K. P. Bennett and O. L. Mangasarian, Robust Linear Programming Discrimination of Two Linearly Inseparable Sets, Optimization Methods and Software, 1:23-24, 1992. |
[3] | O. Chappelle, Training a Support Vector Machine in the Primal, Neural Computation, 2007. |
[4] | M. Collins, R. E. Schapire and Y. Singer, Logistic regression, AdaBoost and Bregman distances, COLT, 2000. |
[5] | R. Cowell, A. David, S. Lauritzen and D. Spiegelhalter, Probabilistic Networks and Expert Systems, Springer, New York, 1999. |
[6] | T. Joachims, A Support Vector Method for Multivariate Performance Measures, ICML, 2005. |
[7] | T. Joachims, Training linear SVMs in linear time, KDD, 2006. |
[8] | V. Vapnik, S. Golowich and A. J. Smola, Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing, NIPS, 1997. |
[9] | K.-R. Mueller, A. J. Smola, G. Raetsch, B. Schoelkopf, J. Kphlmorgen and V. Vapnik, Predicting Time Series with Support Vector Machines, ICANN, 1997. |
[10] | C. K. I. Williams, Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond, M. I. Jordan, editor, Learning and Inference in Graphical Models, 1998. |
[11] | B. Schoelkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor and J. Platt, Support Vector Method for Novelty Detection, NIPS, 2000. |
[12] | R. Koenker, Quantile Regression, Cambridge University Press, 2005. |
[13] | N. A. C. Cressie, Statistics for Spatial Data, John Wiley and Sons, New York, 1993. |
[14] | Q. Le and A. J. Smola, Direct Optimization of Ranking Measures, JMLR, submitted. 2007. |
[15] | R. Herbrich, T. Graepel and K. Obermayer, Large Margin Rank Boundries for Ordinal Regression, Advanced in Large Margin Classifiers, MIT Press, MA, 2000. |
[16] | T. S. Caetano, J. J. McAuley, L. Cheng, Q. V. Le, and A. J. Smola, Learning Graph Matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009. |
[17] | Q. Shi, L. Wang, L. Cheng, and A. J. Smola, Discriminative Human Action Segmentation and Recognition using Semi-Markov Models, CVPR, 2008. |
Last modified: 19 February 2009