Accord.NET

Accord.NET is a framework for scientific computing in .NET. The source code of the project is available under the terms of the Gnu Lesser Public License, version 2.1.

Accord.NET
Original author(s)César Roberto de Souza
Initial releaseMay 20, 2010 (2010-05-20)[1]
Stable release
3.8.0 / October 22, 2017 (2017-10-22)
Preview release
3.8.0 / October 22, 2017 (2017-10-22)
Repository
Written inC#
Operating systemCross-platform
TypeFramework
LicenseLGPLv3 and partly GPLv3
Websitewww.accord-framework.net

The framework comprises a set of libraries that are available in source code as well as via executable installers and NuGet packages. The main areas covered include numerical linear algebra, numerical optimization, statistics, machine learning, artificial neural networks, signal and image processing, and support libraries (such as graph plotting and visualization).[2][3] The project was originally created to extend the capabilities of the AForge.NET Framework, but has since incorporated AForge.NET inside itself. Newer releases have united both frameworks under the Accord.NET name.

The Accord.NET Framework has been featured in multiple books such as Mastering.NET Machine Learning[4] by PACKT publishing and F# for Machine Learning Applications,[5] featured in QCON San Francisco,[6] and currently accumulates more than 1,500 forks in GitHub.[7]

Multiple scientific publications have been published with the use of the framework.[8][9][10][11][12][13]

See also

References
  1. "Machine learning, computer vision, statistics and general scientific computing for .NET: Accord-net/framework". 2018-12-21.
  2. Greg Duncan. Portable Image and Video processing with help from AForge.NET and Accord.NET. Channel 9, November 2014. Web extract
  3. Accord project on Open Hub. Web extract
  4. "Mastering .NET Machine Learning". www.packtpub.com.
  5. "F# for Machine Learning Essentials". www.packtpub.com.
  6. "NET Machine Learning: F# and Accord.NET". InfoQ.
  7. Accord.NET Framework project on GitHub
  8. Blamey, Ben; Crick, Tom; Oatley, Giles (2013). "'The First Day of Summer': Parsing Temporal Expressions with Distributed Semantics" (PDF). Research and Development in Intelligent Systems XXX (PDF). Springer, Cham. pp. 389–402. doi:10.1007/978-3-319-02621-3_29. ISBN 978-3-319-02620-6.
  9. Mueller, Wojciech; Nowakowski, Krzysztof; Tomczak, Robert J.; Kujawa, Sebastian; Rudowicz-Nawrocka, Janina; Idziaszek, Przemysław; Zawadzki, Adrian (2013-07-19). "IT system supporting acquisition of image data used in the identification of grasslands". In Wang, Yulin; Yi, Xie (eds.). Fifth International Conference on Digital Image Processing (ICDIP 2013). 8878. International Society for Optics and Photonics. pp. 88781T–88781T–4. doi:10.1117/12.2031602. S2CID 368511.
  10. Arriaga, Julio; Kossan, George; Cody, Martin; Vallejo, Edgar; Taylor, Charles (2013). Acoustic sensor arrays for understanding bird communication. Identifying Cassin's Vireos using SVMs and HMMs. Advances in Artificial Life, ECAL 2013. pp. 827–828. CiteSeerX 10.1.1.474.7109. doi:10.7551/978-0-262-31709-2-ch120. ISBN 9780262317092.
  11. Keramitsoglou, I.; Kiranoudis, C. T.; Weng, Q. (September 2013). "Downscaling Geostationary Land Surface Temperature Imagery for Urban Analysis". IEEE Geoscience and Remote Sensing Letters. 10 (5): 1253–1257. Bibcode:2013IGRSL..10.1253K. doi:10.1109/lgrs.2013.2257668. ISSN 1545-598X. S2CID 8990560.
  12. Afif, Mohammed H.; Hedar, Abdel-Rahman; Hamid, Taysir H. Abdel; Mahdy, Yousef B. (2012-12-08). Support Vector Machines with Weighted Powered Kernels for Data Classification. Advanced Machine Learning Technologies and Applications. Communications in Computer and Information Science. 322. pp. 369–378. doi:10.1007/978-3-642-35326-0_37. ISBN 978-3-642-35325-3.
  13. De Souza, Cesar Roberto (2017). "Procedural Generation of Videos to Train Deep Action Recognition Networks". Computer Vision and Pattern Recognition (CVPR). 2017: 4757–4767. arXiv:1612.00881 – via CVPR Open Access.
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