Introducing the Accord.NET Framework, a powerful collection of libraries and tools for developers seeking to create dynamic and responsive applications for Windows 7. Developed by renowned software engineer Cesar Roberto de Souza, this cutting-edge framework offers an impressive range of features, including advanced artificial intelligence algorithms, statistical processing tools, and much more. Whether you're an experienced programmer or a newcomer to the field, the Accord.NET Framework is sure to take your applications to the next level.

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 divided in libraries, available either through an executable installer, standalone compressed archives and NuGet packages. Those libraries include:

Scientific Computing

Accord.Math Contains a matrix extension library, along with a suite of numerical matrix decomposition methods, numerical optimization algorithms for constrained and unconstrained problems, special functions and other tools for scientific applications.
Accord.Statistics Probability distributions, statistical models and methods such as Linear and Logistic regression, 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.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.Neuro Neural learning algorithms such as Levenberg-Marquardt, Parallel Resilient Backpropagation, initialization procedures such as Nguyen-Widrow and other neural network related methods.
Signal and Image Processing

Accord.Imaging Interest point detectors (Harris, SURF and FAST), image matching and image stitching methods. Can create integral images and other image transformations, plus additional image filters for image processing a applications.
Accord.Audio Process, transforms, filters and handle audio signals for machine learning and statistical applications.
Accord.Vision Real-time face detection and tracking, as well as general methods for detecting, tracking and transforming objects in image streams. Contains cascade definitions, Camshift and Dynamic Template Matching trackers.
Support Libraries

Accord.Controls Histograms, scatter-plots and tabular data viewers for scientific applications.
Accord.Controls.Imaging Windows Forms controls to show and handle images. Contains a convenient ImageBox control which mimics the traditional MessageBox behavior for quickly displaying or inspecting images.
Accord.Controls.Audio Windows Forms controls to display waveforms and audio-related information.
Accord.Controls.Vision Windows Forms components and controls to track head, face and hand movements and other computer vision related tasks.

New Release
General:
This will be last release that includes an executable installer. If you are still using the installer, please move to NuGet or use the framework compressed archive files.

Imaging:
Creating a new Accord.Imaging.Noncommercial assembly to hold non-commercial imaging methods;
Adding Fast Guided Filter to Accord.Imaging.Noncommercial.

MachineLearning:
Fixing Binary Split's learn method to accept null weights;
Updating Binary Split example to reflect the new API;
Adding constructors to allow tree inducing algorithms to create a tree from scratch;

Statistics:
Fixing multiple issues with statistical analyses classes when they are used using the new classification/regression APIs;
Statistical measures (Measures.cs) have been moved to the Accord.Math assembly,
but have been kept under the Accord.Statistics namespace;
Correcting L2-regularization in Logistic Regression.
[ Accord.NET Framework release history ]

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