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LIBSVM is a library for Support Vector Machines (SVMs). LIBSVM offers tools such as Multi-core LIBLINEAR, Distributed LIBLINEAR, LIBLINEAR for Incremental and Decremental Learning, LIBLINEAR for One-versus-one Multi-class Classification.
DataMining Software Free
• Different SVM formulations • Efficient multi-class classification • Cross validation for model selection • Probability estimates • Various kernels (including precomputed kernel matrix) • Weighted SVM for unbalanced data • Both C++ and Java sources • GUI demonstrating SVM classification and regression • Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, OCaml, LabVIEW, and PHP interfaces. C# .NET code and CUDA extension is available. • It's also included in some data mining environments: RapidMiner, PCP, and LIONsolver. • Automatic model selection which can generate contour of cross validation accuracy.
Small (<50 employees), Medium (50 to 1000 Enterprise (>1001 employees)
• Different SVM formulations • Efficient multi-class classification • Cross validation for model selection • Probability estimates • Various kernels (including precomputed kernel matrix) • Weighted SVM for unbalanced data • Both C++ and Java sources
What are the benefits?
• Solving SVM optimization problems, • Solving theoretical convergence, • Solving multi-class classiﬁcation, • Solving probability estimates, • Solving parameter selection are discussed in detail
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LIBSVM involves training a data set to obtain a model, using the model to predict information of a testing data set and can also output probability estimates for SVC and SVR.
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LIBSVM is a library for Support Vector Machines (SVMs). LIBSVM offers tools such as Multi-core LIBLINEAR, Distributed LIBLINEAR, LIBLINEAR for Incremental and Decremental Learning, LIBLINEAR for One-versus-one Multi-class Classification, Large-scale rankSVM, LIBLINEAR for more than 2^32 instances/features (experimental), Large linear classification when data cannot fit in memory, Weights for data instances.
SVM Multi-class Probability Outputs, An integrated development environment to libsvm, ROC Curve for Binary SVM, Grid Parameter Search for Regression, Radius Margin Bounds for SVM Model Selection, Reduced Support Vector Machines Implementation, LIBSVM for SVDD and finding the smallest sphere containing all data and DAG approach for multiclass classification.
LIBSVM supports various SVM formulations for classiﬁcation, regression, and distribution estimation. LIBSVM provides a simple sub-sampling tool, so users can quickly train a small subset but LIBSVM may take considerable training time for huge data sets. LIBSVM provides a simple parameter tool to check a grid of parameters where each parameter setting, LIBSVM obtains cross-validation (CV) accuracy.
LIBSVM outputs the contour plot of cross-validation accuracy. LIBSVM becomes a complete SVM package which includes other SVM variants, and supported functions such as multi-class classiﬁcation and probability estimates. LIBSVM supports learning tasks such as support vector regression and One-class SVM.
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