jmlr jmlr2013 jmlr2013-19 jmlr2013-19-reference knowledge-graph by maker-knowledge-mining
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Author: Nemanja Djuric, Liang Lan, Slobodan Vucetic, Zhuang Wang
Abstract: 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. Keywords: non-linear classification, large-scale learning, SVM, machine learning toolbox
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