Project details for Accord.NET Framework

Screenshot Accord.NET Framework 2.10.0

by cesarsouza - September 9, 2013, 16:12:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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This framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. This project extends the popular AForge.NET Framework providing a more complete scientific computing environment.

  • Accord.Math - Contains a matrix extension library, along with a suite of numerical matrix decomposition methods, numerical optimization algorithms for contrained and uncontrained problems, special functions and other tools for scientific applications;
  • Accord.Statistics - Contains probability distributions, statistical models and methods such as Linear and Logistic regressions, Hidden Markov Models, (Hidden) Conditional Random Fields, Principal Component Analysis, Partial Least Squares, Discriminant Analysis, Kernel methods and functions and many other related techniques;
  • Accord.Imaging - Interest point detectors (SURF and FAST), image matching and image stitching methods;
  • Accord.Neuro - Neural learning algorithms such as Levenberg-Marquardt, Parallel Resilient Backpropagation, initialization procedures such as Nguyen-Widrow and other neural network related methods;
  • Accord.MachineLearning - Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and Grid-Search for machine-learning applications;
  • Accord.Vision - Real-time face detection and tracking, as well as general methods for detecting, tracking and transforming objects in image streams. Contains Haar cascade definitions, Camshift and Dynamic Template Matching trackers;
  • Accord.Audio - Process, transforms, filters and handle audio signals for machine learning and statistical applications.

For a complete listing of framework features, please see the feature list at

Packages are also now available through NuGet -

Changes to previous version:

This release aimed to provide improvements to the documentation. Most of the Univariate Distributions now include proper examples for all main functions and measures in their summary page. Also, a wide set of imaging methods, such as Haralick's set of textural features, the Local Binary Pattern, Gabor, Kirsch, and Variance filters have been added. Also includes the Denavit-Hartenberg model for kinematic chains and many updates, optimizations, corrections and bug-fixes in all major namespaces.

For a complete list of changes, please see the full release notes at the release details page at:

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Linux, Windows
Data Formats: Agnostic
Tags: Svm, Kernel Methods, Algorithms, Classifiers, Statistics, Clustering Algorithm, Probability Estimation, Discriminant Analysis, Wavelet Transform, Principal Component Analysis, Algebra, Fourier
Archive: download here


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