About:
The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details.
Changes:
Adding a large number of new distributions, such as Anderson-Daring, Shapiro-Wilk, Inverse Chi-Square, Lévy, Folded Normal, Shifted Log-Logistic, Kumaraswamy, Trapezoidal, U-quadratic and BetaPrime distributions, Birnbaum-Saunders, Generalized Normal, Gumbel, Power Lognormal, Power Normal, Triangular, Tukey Lambda, Logistic, Hyperbolic Secant, Degenerate and General Continuous distributions.
Other additions include new statistical hypothesis tests such as Anderson-Daring and Shapiro-Wilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others.
For a complete list of changes, please see the full release notes at the release details page at:
https://github.com/accord-net/framework/releases
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- Operating System:
Linux,
Windows,
Platform Independent
- Data Formats:
Matlab,
Agnostic,
Various,
Excel,
Mat,
Libsvm Format
- Tags:
Svm,
Clustering,
Crf,
Kernel Methods,
Convex Optimization,
Deep Belief Networks,
Deep Learning,
Decision Trees,
Algorithms,
Classifiers,
Data Mining,
Data Formats,
Conditional Random Fields,
Statistics,
Clustering Algorithm,
Probability Estimation,
Discriminant Analysis,
Wavelet Transform,
Principal Component Analysis,
Algebra,
Fourier
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