Support vector machines on GPU with sparse matrix format

Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Volumn , Issue , 2010, Pages 313-318

  Lin, Tsung Kai ^a   Chien, Shao Yi ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

GPGPU; Sparse matrix multiplication; Support vector machines

Indexed keywords

GPGPU; GRAPHICS PROCESSING UNIT; MACHINE LEARNING ALGORITHMS; MULTI-CORE PLATFORMS; SPARSE MATRICES; SUPPORT VECTOR; TRAINING PROCESS;

COMPUTER GRAPHICS EQUIPMENT; LEARNING ALGORITHMS; PROGRAM PROCESSORS; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 79952402900     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2010.53     Document Type: Conference Paper

References (19)

1. C. Cortes and V. Vapnik, "Support vector networks," Machine Learning, vol. 20, pp. 273-297, 1995.
2. J. C. Platt, "Fast training of support vector machines using sequential minimal optimization," pp. 185-208, 1999.
3. T. Joachims, "Making large-scale support vector machine learning practical," 1998.
4. R.-E. Fan, P.-H. Chen, and C.-J. Lin, "Working set selection using second order information for training support vector machines," J. Mach. Learn. Res., vol. 6, pp. 1889-1918, 2005.
5. C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines, 2001, software available at http://www.csie.ntu.edu.tw/cjlin/libsvm.
6. B. Catanzaro, N. Sundaram, and K. Keutzer, "Fast support vector machine training and classification on graphics processors," in ICML '08: Proceedings of the 25th international conference on Machine learning. New York, NY, USA: ACM, 2008, pp. 104-111.
7. S. Herrero-Lopez, J. Williams, and A. Sanchez, "Parallel multiclass classification using SVMs on GPUs," in Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units. ACM, 2010, pp. 2-11.
8. L. Bottou, C. Cortes, J. Denker, H. Drucker, I. Guyon, L. Jackel, Y. LeCun, U. Muller, E. Sackinger, P. Simard et al., "Comparison of classifier methods: a case study in handwritten digit recognition," in Conference on Pattern Recognition, 1994. Vol. 2-Conference B: Computer Vision & Image Processing., Proceedings of the 12th IAPR International, 1994, pp. 77-82.
9. J. H. Friedman, "Another approach to polychotomous classification," Department of Statistics, Stanford University, Tech. Rep., 1996. [Online]. Available: http://wwwstat.stanford.edu/ jhf/ftp/poly.ps.Z
10. J. C. Platt, N. Cristianini, and J. Shawe-taylor, "Large margin dags for multiclass classification," in Advances in Neural Information Processing Systems. MIT Press, 2000, pp. 547-553.
11. J. Weston and C. Watkins, "Multi-class support vector machines," 1998.
12. C. Hsu and C. Lin, "A comparison of methods for multi-class support vector machines," IEEE transactions on Neural Networks, vol. 13, no. 2, pp. 415-425, 2002.
13. C. Nvidia, "Compute Unified Device Architecture Programming Guide," NVIDIA: Santa Clara, CA, 2007.
14. V. Volkov and J. W. Demmel, "Benchmarking gpus to tune dense linear algebra," in SC '08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing. Piscataway, NJ, USA: IEEE Press, 2008, pp. 1-11.
15. N. Bell and M. Garlandy, "Efficient sparse matrix-vector multiplication on CUDA," NVIDIA Corporation, Santa Clara, CA, USA, Tech. Rep., December 2008.
16. F. Vázquez, E. Garzon, J. Martinez, and J. Fernandez, "The sparse matrix vector product on GPUs," Technical Report, University of Almeria, Tech. Rep., 2009.
17. A. Frank and A. Asuncion, "UCI machine learning repository," 2010. [Online]. Available: http://archive.ics.uci.edu/ml
18. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," in Proceedings of the IEEE, 1998, pp. 2278-2324.
19. J. Hull, "A database for handwritten text recognition research," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 16, pp. 550-554, 1994.