Hypertable is an open source project based on published best practices and our own experience in solving large-scale data-intensive tasks. Our goal is nothing less than that Hypertable become the world's most massively parallel high performance database platform.
Fast Artificial Neural Network Library is a free open source neural network library, which implements multilayer artificial neural networks in C with support for both fully connected and sparsely connected networks. Cross-platform execution in both fixed and floating point are supported. It includes a framework for easy handling of training data sets. It is easy to use, versatile, well documented, and fast. PHP, C++, .NET, Ada, Python, Delphi, Octave, Ruby, Prolog Pure Data and Mathematica bindings are available. A reference manual accompanies the library with examples and recommendations on how to use the library. A graphical user interface is also available for the library.
E. Yeh, D. Ramage, C. Manning, E. Agirre, and A. Soroa. Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing, page 41--49. Stroudsburg, PA, USA, Association for Computational Linguistics, (2009)
R. West, J. Pineau, and D. Precup. Proceedings of the 21st international jont conference on Artifical intelligence, page 1598--1603. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (2009)
A. Java, X. Song, T. Finin, and B. Tseng. Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, page 56--65. New York, NY, USA, ACM, (2007)
H. Kwak, C. Lee, H. Park, and S. Moon. Proceedings of the 19th international conference on World wide web, page 591--600. New York, NY, USA, ACM, (2010)
J. Kunegis, A. Lommatzsch, and C. Bauckhage. Proceedings of the 18th international conference on World wide web, page 741--750. New York, NY, USA, ACM, (2009)
C. Cattuto, D. Benz, A. Hotho, and G. Stumme. Proceedings of the 7th International Conference on The Semantic Web, page 615--631. Berlin, Heidelberg, Springer-Verlag, (2008)
J. Alstott, E. Bullmore, and D. Plenz. (2013)cite arxiv:1305.0215Comment: 15 pages, 6 figures, supporting information at https://pypi.python.org/pypi/powerlaw.
J. Leskovec, L. Backstrom, R. Kumar, and A. Tomkins. KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, page 462--470. New York, NY, USA, ACM, (2008)