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The Accord.NET Framework is a C# framework extending the excellent AForge.NET Framework external 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 external.

The framework is comprised by libraries and sample applications demonstrating their features. Some of the libraries include:

* Accord.Math - General math and matrix library with numerical decompositions and related linear algebra methods;
* Accord.Statistics - library with statistical analysis and related tools;
* Accord.Imaging - extension to the AForge.NET Imaging library with new filters and routines;
* Accord.Neuro - extension to the AForge.NET Neuro library with other learning algorithms;
* Accord.MachineLearning - extension to AForge's machine learning library with SVMs and related techniques;
* Accord.Vision - extension to the AForge.NET Vision library with realtime object detectors and trackers;
* Accord.Audio - experimental library with signal filters and audio processing routines.