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 ]

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Description:

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 mloss.org.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
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

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