BudgetedSVM: a toolbox for scalable SVM approximations. 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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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Horn, Daniel; Demircioğlu, Aydın; Bischl, Bernd; Glasmachers, Tobias; Weihs, Claus: A comparative study on large scale kernelized support vector machines (2018)
- Zhang, Quan; Zhou, Mingyuan: Permuted and augmented stick-breaking Bayesian multinomial regression (2018)
- Ingo Steinwart, Philipp Thomann: liquidSVM: A Fast and Versatile SVM package (2017) arXiv
- Djuric, Nemanja; Lan, Liang; Vucetic, Slobodan; Wang, Zhuang: BudgetedSVM: a toolbox for scalable SVM approximations (2013)