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

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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