Description:

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

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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