Description:

A C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

The library provides efficient implementations of the following algorithms:

• support vector machines for classification
• relevance vector machines for regression and classification
• reduced set approximation of SV decision surfaces
• online kernel RLS regression
• online kernelized centroid estimation/one class classifier
• kernel k-means clustering
• radial basis function networks
• kernelized recursive feature ranking
• Bayesian network inference using junction trees or MCMC

The library also comes with extensive documentation and example programs that walk the user through the use of these machine learning techniques.

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Supported Operating Systems:

Linux, Macosx, Windows, Unix, Solaris

Tags:

Svm, Classification, Clustering, Regression, Kernel Methods, Matrix Library, Kkmeans, Optimization, Algorithms, Exact Bayesian Methods, Approximate Inference, Bayesian Networks, Junction Tree

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