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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.
Features
- 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.