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.

dlib also comes with a fast matrix library that lets the user use a simple Matlab like syntax. It is also capable of using BLAS libraries such as ATLAS or the Intel MKL when available. Additionally, the use of BLAS is transparent to the user, that is, the dlib matrix object uses BLAS internally to optimize all the various forms of matrix multiplication while still allowing the user to use a simple Matlab like syntax.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download

Supported Operating Systems: Linux, Macosx, Windows, Unix, Solaris

Data Formats: None

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

Archive: download here

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