The Accord.NET Framework is a C# framework extending the excellent AForge.NET Framework with new tools and libraries. Accord.NET provides many algorithms for many topics in mathematics, statistics, machine learning, artificial intelligence and computer vision. It includes several methods for statistical analysis, such as Principal Component Analysis, Linear Discriminant Analysis, Partial Least Squares, Kernel Principal Component Analysis, Kernel Discriminant Analysis, Logistic and Linear Regressions and Receiver-Operating Curves. It also includes machine learning topics such as (Kernel) Support Vector Machines, Bayesian regularization for Neural Network training, RANSAC, K-Means, Gaussian Mixture Models and Discrete and Continuous Hidden Markov Models. The imaging and computer vision libraries includes projective image blending, homography estimation, the Camshift object tracker and the Viola-Jones object detector.

Update: The project page is currently being transferred to Google Code.

This project is in no way affiliated with AForge.NET Framework nor AForge.NET authors or contributors have any affiliation with this project. The extensions given by this framework and the framework itself are released under a LGPL license. This software is provided "as is" and any express or implied warranties, including, but no limited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed. In no event shall the authors or the contributors of this software be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this software, even if advised of the possibility of such damage.