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
- Squared hinge
- Logistic regression
- ROC Score
- Fbeta Score
- Univariate regression
- Huber robust
- Least Mean Squares
- Least Absolute Deviation
- Novelty detection (1-class SVM)
- Quantile regression
- Poisson regression
- 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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