Added	19 Mar 2009
Updated	09 Jan 2010
Authors	Davis E. King
OS	All OS
Language	C++

dlib is a toolkit containing algorithms and tools that facilitate creating complex software in C++ to solve real world problems.

The library provides efficient implementations of the following machine learning 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
• online SVM classification
• 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 comes with a fast matrix library that lets the user use a simple Matlab like syntax.  It is 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.