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2013 JMLR JMLR 2013

BudgetedSVM: A Toolbox for Scalable SVM Approximations

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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2013. (edit, beta)

🧭 Keyword Pioneer — low-rank linearization
🐣 Hot Topic Early Bird — stochastic gradient descent
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio

Authors

Nemanja Djuric , Liang Lan , Slobodan Vucetic , Zhuang Wang

Topics

Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Optimization Machine Learning > Application Areas > Efficient Computing Machine Learning > Optimization & Theory > Stochastic Methods Machine Learning > Core Methods > Support Vector Machine

Keywords

stochastic gradient descent budgeted learning scalable training support vector machine low-rank linearization
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