BMRM 2.1

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

Main features:
- 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