jmlr jmlr2011 jmlr2011-102 jmlr2011-102-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Michael Gashler
Abstract: We present a breadth-oriented collection of cross-platform command-line tools for researchers in machine learning called Waffles. The Waffles tools are designed to offer a broad spectrum of functionality in a manner that is friendly for scripted automation. All functionality is also available in a C++ class library. Waffles is available under the GNU Lesser General Public License. Keywords: machine learning, toolkits, data mining, C++, open source
A. Blum and S. Chawla. Learning from labeled and unlabeled data using graph mincuts. In Proceedings of the Eighteenth International Conference on Machine Learning, pages 19–26. Morgan Kaufmann Publishers Inc., 2001. ISBN 1558607781. M. Gashler, C. Giraud-Carrier, and T. Martinez. Decision tree ensemble: Small heterogeneous is better than large homogeneous. In Seventh International Conference on Machine Learning and Applications, 2008. ICMLA ’08., pages 900–905. Dec. 2008. doi: 10.1109/ICMLA.2008.154. M. Gashler, D. Ventura, and T. Martinez. Manifold learning by graduated optimization. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, PP(99):1 –13, 2011a. ISSN 1083-4419. doi: 10.1109/TSMCB.2011.2151187. M. Gashler, D. Ventura, and T. Martinez. Tangent space guided intelligent neighbor finding. In The 2011 International Joint Conference on Neural Networks (IJCNN), July 2011b. M. Gashler, D. Ventura, and T. Martinez. Temporal nonlinear dimensionality reduction. In The 2011 International Joint Conference on Neural Networks (IJCNN), July 2011c. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten. The WEKA data mining software: an update. SIGKDD Explor. Newsl., 11(1):10–18, 2009. ISSN 1931-0145. doi: 10. 1145/1656274.1656278. S. Roweis and L. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323–2326, 2000. S. Sonnenburg, M.L. Braun, C.S. Ong, S. Bengio, L. Bottou, G. Holmes, Y. LeCun, K.R. M¨ ller, u F. Pereira, C.E. Rasmussen, et al. The need for open source software in machine learning. Journal of Machine Learning Research, 8:2443–2466, 2007. J. Tenenbaum, V. de Silva, and J. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, 290:2319–2323, 2000. 2387