Description:

Accord.NET Framework is a C# framework which extends the excellent AForge.NET Framework with new tools and libraries.

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, such as Nonnegative Matrix Factorization, generalized Eigenvalue and others;
• Accord.Statistics - library with statistical analysis and related tools, such as (Kernel) component and discriminant analysis, partial least squares and hidden Markov models;
• Accord.Imaging - extension to the AForge.NET Imaging library with new filters and routines;
• Accord.Neuro - extension to the AForge.NET Neuro library with new algorithms such as Levenberg-Marquardt with Bayesian regularization and Nguyen-Widrow weight initialization;
• Accord.MachineLearning - extension to AForge's machine learning library with SVMs, K-Means and GMM clustering and related techniques;
• Accord.Vision - extension to the AForge.NET Vision library with realtime object detectors and trackers such as Viola-Jones and Camshift;
• Accord.Audio - experimental library with filters and audio processing routines.

For a complete listing of framework features, please see the feature list at http://accord-net.origo.ethz.ch/wiki/features

Changes to previous version:

The various works on this release have introduced some breaking interface changes, mainly in the Audio and Statistics namespaces.

• Adding support for Independent Component Analysis;
• Adding observation prediction in hidden Markov models;
• Major reorganization of the hidden Markov models namespace;
• Major architectural changes on the Accord.Audio namespace;
• Adding a new algorithm for LDLt Cholesky matrix decomposition;
• Adding Sparse versions of Gaussian, Polynomial, Laplacian, Sigmoid and Cauchy kernels.

And several other bugfixes and enhancements. For a complete list of changes, please see the full release notes at http://accord-net.origo.ethz.ch/download/2822

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 Transfo

Archive: download here

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