BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes three methods
* One vs. One multi-class classification using a bound-constrained formulation * Multi-class classification by solving a single optimization problem (again, a bounded formulation). * Multi-class classification using Crammer and Singer's formulation. * Regression using a bound-constrained formulation
It also has an efficient implementation for linear SVMs.
The current implementation borrows the structure of libsvm. Similar options are also adopted. For the bound-constrained formulation for classification and regression, BSVM uses a decomposition method. BSVM uses a simple working set selection which leads to faster convergences for difficult cases. The use of a special implementation of the opmization solver TRON allows BSVM to stably identify bounded variables.
- Changes to previous version:
Initial Announcement on mloss.org.
- BibTeX Entry: Download
- Corresponding Paper BibTeX Entry: Download
- URL: Project Homepage
- Supported Operating Systems: Cygwin, Linux, Windows, Macos
- Data Formats: None
- Tags: Classification, Regression, Support Vector Machines, Kernel Methods, Multi Class, Linear Svm, Large Scale Learning
- Archive: download here
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