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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Description:

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 https://code.google.com/p/accord/wiki/Features

Packages are also now available through NuGet - http://nuget.org/packages?q=Accord.NET

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:

https://github.com/accord-net/framework/releases

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
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

Other available revisons

Version Changelog Date
2.10.0

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:

https://github.com/accord-net/framework/releases

September 9, 2013, 16:12:01
2.8.0

This release brings Cox's proportional hazards models and the partial Newton-Raphson learning algorithm. It also provides a reorganization of the (Hidden Conditional Random) Fields namespace, together with more bugfixes, improvements and optimizations.

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

November 6, 2012, 07:01:01
2.7.1

This release fixes few bugs, adds new clustering algorithms, and provides a compatibility package to work with .NET 3.5.

For a complete list of changes, please see the full release notes at https://code.google.com/p/accord/source/browse/trunk/Release%20notes.txt

September 13, 2012, 01:57:18
2.6.0

This release adds support for RProp learning in HCRFs, optimizations to SVM learning and evaluation, a constrained QP solver based on the dual method of Goldfrab and Idnani, robust estimation of fundamental matrices 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/3982

April 4, 2012, 16:36:42
2.1.5

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

February 21, 2011, 14:49:49
2.1.4
  • Adding support for more Haar cascade classifier definitions;
  • Optimizing Levenberg-Marquardt learning algorithm;

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/2312

December 3, 2010, 22:17:04
2.1.3
  • Adding Non-Negative Matrix Factorization, Continuous density Hidden Markov Models and Gaussian Mixture Models;
  • Heavy work on documentation.
November 3, 2010, 14:35:27
2.1.2

Initial Announcement on mloss.org.

October 16, 2010, 15:33:39

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