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
• 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 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:

This release adds a general purpose implementation of the OCA optimizer, OCAS SVM trainer, and support for loading and saving LIBSVM formatted data files.

BibTeX Entry: Download

Corresponding Paper BibTeX Entry: Download

URL: Project Homepage

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

Other available revisons

Version	Changelog	Date
17.26	This release adds a general purpose implementation of the OCA optimizer, OCAS SVM trainer, and support for loading and saving LIBSVM formatted data files.	March 7, 2010, 21:37:42
17.25	This release was primarily devoted to bug fixing and usability improvements.	February 6, 2010, 00:54:08
17.24	This release improves the documentation, adds a few example programs, and fixed some bugs. Additionally, the following new components have been added: A semi-supervised PCA A tool for computing empirical kernel maps The BOBYQA algorithm for box constrained derivative-free optimization	January 5, 2010, 01:17:24
17.22	This release adds a kernel cache and support for training on highly unbalanced data to the PEGASOS SVM training module. Additionally, the library now includes an implementation of the L-BFGS algorithm for unconstrained optimization.	September 11, 2009, 04:09:11
17.20	This release has been focused mostly on maintenance and usability improvements.	July 14, 2009, 03:04:23
17.18	This release adds kernels capable of operating on sparse vectors. It also includes various optimizations targeted at speeding up algorithms that operate with linear kernels.	April 6, 2009, 00:57:16
17.17	This release has two main updates: The Pegasos SVM training implementation now has a parameter to allow the user to directly control the number of output support vectors. The documentation has been cleaned up and example programs updated	March 17, 2009, 03:49:11
17.16	In addition to a lot of small usability improvements, this release adds a sparse kernelized version of the Pegasos SVM training algorithm.	March 9, 2009, 02:21:49
17.15	Initial Announcement on mloss.org.	June 13, 2008, 13:55:10

Comments

No one has posted any comments yet. Perhaps you'd like to be the first?

Leave a comment

You must be logged in to post comments.