Large linear classification when data cannot fit in memory

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2010, Pages 833-842

  Yu, Hsiang Fu ^a   Hsieh, Cho Jui ^a   Chang, Kai Wei ^a   Lin, Chih Jen ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Algorithms; Experimentation; Performance

Indexed keywords

COMPUTER MEMORIES; DATA SETS; DESIGN CONSIDERATIONS; DOCUMENT CLASSIFICATION; EXPERIMENTATION; LEARNING METHODS; LINEAR CLASSIFICATION; MEMORY CAPACITY; MEMORY SIZE; PERFORMANCE; RANDOM ACCESS; TRAINING METHODS;

ALGORITHMS; DISKS (STRUCTURAL COMPONENTS); EXPERIMENTS; INFORMATION RETRIEVAL SYSTEMS;

COMPUTERS;

EID: 77956195198     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1835804.1835910     Document Type: Conference Paper

References (20)

1. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin, "LIBLINEAR: A library for large linear classification," JMLR, vol. 9, pp. 1871-1874, 2008.
2. T. Joachims, "Training linear SVMs in linear time," in ACM KDD, 2006.
3. S. Shalev-Shwartz, Y. Singer, and N. Srebro, "Pegasos: primal estimated sub-gradient solver for SVM," in ICML, 2007.
4. L. Bottou, "Stochastic gradient descent examples," 2007. http://leon.bottou.org/projects/sgd.
5. C.-J. Hsieh, K.-W. Chang, C.-J. Lin, S. S. Keerthi, and S. Sundararajan, "A dual coordinate descent method for large-scale linear SVM," in ICML, 2008.
6. J. Langford, L. Li, and T. Zhang, "Sparse online learning via truncated gradient," JMLR, vol. 10, pp. 771-801, 2009.
7. E. Chang, K. Zhu, H. Wang, H. Bai, J. Li, Z. Qiu, and H. Cui, "Parallelizing support vector machines on distributed computers," in NIPS 21, 2007.
8. Z. A. Zhu, W. Chen, G. Wang, C. Zhu, and Z. Chen, "P-packSVM: Parallel primal gradient descent kernel SVM," in ICDM, 2009.
9. J. Langford, A. J. Smola, and M. Zinkevich, "Slow learners are fast," in NIPS, 2009.
10. H. Yu, J. Yang, and J. Han, "Classifying large data sets using SVMs with hierarchical clusters," in ACM KDD, 2003.
11. D. P. Bertsekas, Nonlinear Programming. Belmont, MA 02178-9998: Athena Scientific, second ed., 1999.
12. 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.
13. T. Joachims, "Making large-scale SVM learning practical," in Advances in Kernel Methods - Support Vector Learning, MIT Press, 1998.
14. R. Memisevic, "Dual optimization of conditional probability models," tech. rep., Department of Computer Science, University of Toronto, 2006.
15. F. Pérez-Cruz, A. R. Figueiras-Vidal, and A. Artés- Rodríguez, "Double chunking for solving SVMs for very large datasets," in Proceedings of Learning 2004, Spain, 2004.
16. S. Rüping, "mySVM - another one of those support vector machines," 2000. Software available at http://www-ai.cs.uni-dortmund.de/ SOFTWARE/MYSVM/.
17. K. G. Morse, Jr., "Compression tools compared," Linux Journal, 2005.
18. K. Crammer and Y. Singer, "On the learnability and design of output codes for multiclass problems," in COLT, 2000.
19. P. Rai, H. Daumé III, and S. Venkatasubramanian, "Streamed learning: One-pass SVMs," in IJCAI, 2009.
20. Z.-Q. Luo and P. Tseng, "On the convergence of coordinate descent method for convex differentiable minimization," J. Optim. Theory Appl., vol. 72, no. 1, pp. 7-35, 1992.