Project details for BudgetedSVM

Logo JMLR BudgetedSVM v1.1

by nemanja - February 12, 2014, 20:53:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Description:

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.

Overview of the main features of the BudgetedSVM package are listed as follows:

  • We provide efficient implementations of algorithms for highly-scalable non-linear SVM training.
  • The toolbox can handle large-scale, high-dimensional data sets that cannot be loaded into memory.
  • The toolbox requires constant memory to train accurate 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-scale, 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.
Changes to previous version:

Changed license from LGPL v3 to Modified BSD.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
JMLR MLOSS PaperURL: JMLR-MLOSS Paper Homepage
Supported Operating Systems: Linux, Windows, Mac Os X
Data Formats: Libsvm Format
Tags: Svm, Large Scale Learning, Big Data, Machine Learning Toolbox, Nonlinear Classification
Archive: download here

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