BudgetedSVM: A Toolbox for Scalable SVM Approximations

Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang.

Year: 2013, Volume: 14, Issue: 84, Pages: 3813−3817


We present BudgetedSVM, an open-source C++ toolbox comprising highly-optimized implementations of recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines, Low-rank Linearization SVM, and Budgeted Stochastic Gradient Descent. BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, solving non-linear problems with millions of high-dimensional examples within minutes on a regular computer. We provide command-line and Matlab interfaces to BudgetedSVM, an efficient API for handling large-scale, high- dimensional data sets, as well as detailed documentation to help developers use and further extend the toolbox.

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