LIBSVM

LIBSVM is a library for Support Vector Machines (SVMs). LIBSVM offers tools such as Multi-core LIBLINEAR, Distributed LIBLINEAR, LIBLINEAR for Incremental and Decremental Learning, LIBLINEAR for One-versus-one Multi-class Classification, Large-scale rankSVM, LIBLINEAR for more than 2^32 instances/features (experimental), Large linear classification when data cannot fit in memory, Weights for data instances.

Fast training/testing for polynomial mappings of data, Cross Validation with Different Criteria (AUC, F-score), Cross Validation using Higher-level Information to Split Data, LIBSVM for dense data, LIBSVM for string data, Multi-label classification, LIBSVM Extensions at Caltech, Feature selection tool, LIBSVM data sets, SVM-toy based on Javascript, SVM-toy in 3D, Multi-class classification (and probability output) via error-correcting codes.

SVM Multi-class Probability Outputs, An integrated development environment to libsvm, ROC Curve for Binary SVM, Grid Parameter Search for Regression, Radius Margin Bounds for SVM Model Selection, Reduced Support Vector Machines Implementation, LIBSVM for SVDD and finding the smallest sphere containing all data and DAG approach for multiclass classification.

LIBSVM supports various SVM formulations for classification, regression, and distribution estimation. LIBSVM provides a simple sub-sampling tool, so users can quickly train a small subset but LIBSVM may take considerable training time for huge data sets. LIBSVM provides a simple parameter tool to check a grid of parameters where each parameter setting, LIBSVM obtains cross-validation (CV) accuracy.

LIBSVM outputs the contour plot of cross-validation accuracy. LIBSVM becomes a complete SVM package which includes other SVM variants, and supported functions such as multi-class classification and probability estimates. LIBSVM supports learning tasks such as support vector regression and One-class SVM.