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BudgetedSVM: A C++ Toolbox for Large-scale, Non-linear Classification

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We present BudgetedSVM, a C++ toolbox containing highly optimized implementations of three recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines (AMM), Budgeted Stochastic Gradient Descent (BSGD), and Low-rank Linearization SVM (LLSVM). BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, as it allows solving highly non-linear classi fication problems with millions of high-dimensional examples within minutes on a regular personal computer. We provide command-line and Matlab interfaces to BudgetedSVM, 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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  • We provide efficient implementations of algorithms for highly-scalable non-linear SVM training.
  • The toolbox can handle large, high-dimensional data sets that cannot be loaded into memory.
  • The toolbox requires constant memory to train models that solve highly non-linear problems.
  • We provide command-line and Matlab interfaces to BudgetedSVM.
  • We provide an efficient API that provides functionalities for handling large, high-dimensional data sets. Using BudgetedSVM API, data sets with millions data points and/or features are easily handled.
  • For more details, please see the documentation included in the download package.
  • Published under industry-friendly Modified BSD licence.


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Intended Audience

Information Technology, Science/Research, End Users/Desktop

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