Parallel multiclass classification using SVMs on GPUs
暂无分享,去创建一个
[1] S. Sathiya Keerthi,et al. Parallel sequential minimal optimization for the training of support vector machines , 2006, IEEE Trans. Neural Networks.
[2] Patrice Y. Simard,et al. Using GPUs for machine learning algorithms , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).
[3] Thorsten Joachims,et al. Making large-scale support vector machine learning practical , 1999 .
[4] Hamid Laga,et al. CUDA (Computer Unified Device Architecture) , 2009 .
[5] Lei Yuan,et al. A Novel Model of Working Set Selection for SMO Decomposition Methods , 2007, 19th IEEE International Conference on Tools with Artificial Intelligence(ICTAI 2007).
[6] Jonathan J. Hull,et al. A Database for Handwritten Text Recognition Research , 1994, IEEE Trans. Pattern Anal. Mach. Intell..
[7] S. Sathiya Keerthi,et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.
[8] W. Daniel Hillis,et al. Data parallel algorithms , 1986, CACM.
[9] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[10] Vladimir Cherkassky,et al. The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.
[11] Kevin Skadron,et al. Scalable parallel programming , 2008, 2008 IEEE Hot Chips 20 Symposium (HCS).
[12] Vwani P. Roychowdhury,et al. Distributed Parallel Support Vector Machines in Strongly Connected Networks , 2008, IEEE Transactions on Neural Networks.
[13] Shirley Dex,et al. JR 旅客販売総合システム(マルス)における運用及び管理について , 1991 .
[14] Federico Girosi,et al. An improved training algorithm for support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.
[15] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[16] J. Kulpa,et al. Time-frequency analysis using NVIDIA compute unified device architecture (CUDA) , 2009, Symposium on Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments (WILGA).
[17] Igor Durdanovic,et al. Parallel Support Vector Machines: The Cascade SVM , 2004, NIPS.
[18] Kurt Keutzer,et al. Fast support vector machine training and classification on graphics processors , 2008, ICML '08.
[19] Thorsten Joachims,et al. Training linear SVMs in linear time , 2006, KDD '06.
[20] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[21] Luca Zanni,et al. Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems , 2006, J. Mach. Learn. Res..
[22] Ryan M. Rifkin,et al. In Defense of One-Vs-All Classification , 2004, J. Mach. Learn. Res..
[23] François Poulet,et al. Speed Up SVM Algorithm for Massive Classification Tasks , 2008, ADMA.
[24] Rafael Mayo,et al. Evaluation and tuning of the Level 3 CUBLAS for graphics processors , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.
[25] Mark J. Harris. Mapping computational concepts to GPUs , 2005, SIGGRAPH Courses.
[26] Chih-Jen Lin,et al. A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.
[27] Chih-Jen Lin,et al. Working Set Selection Using Second Order Information for Training Support Vector Machines , 2005, J. Mach. Learn. Res..
[28] Erik Lindholm,et al. NVIDIA Tesla: A Unified Graphics and Computing Architecture , 2008, IEEE Micro.