Project details for BMRM

Logo BMRM 2.1

by chteo - May 8, 2009, 08:08:20 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

view (1 today), download ( 0 today ), 0 subscriptions


BMRM is an open source, modular and scalable convex solver for many machine learning problems cast in the form of regularized risk minimization problem. 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:

  • Binary classification
  • Hinge
  • Squared hinge
  • Huber-hinge
  • Logistic regression
  • Exponential
  • ROC Score
  • Fbeta Score
  • Univariate regression
  • Epsilon-insensitive
  • Huber robust
  • Least Mean Squares
  • Least Absolute Deviation
  • Novelty detection (1-class SVM)
  • Quantile regression
  • Poisson regression
  • Ranking
  • NDCG (normalized discounted cummulative gain)
  • Graph Matching
  • Sequence Segmentation and Classification

along with either L1 or L2 regularizer. Also, users can add new loss function for problems with structured input and output variables.

Changes to previous version:

Initial Announcement on

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Posix
Data Formats: Svmlight
Tags: Svm, Classification, Regression, Multi Class, Large Scale Learning, Multilabel, Ranking, Optimization, Bundle Methods, Cutting Plane
Archive: download here


No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.