Linear and kernel classification: When to use which?

16th SIAM International Conference on Data Mining 2016, SDM 2016

Volumn , Issue , 2016, Pages 216-224

  Huang, Hsin Yuan ^a   Lin, Chih Jen ^a  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; LEARNING SYSTEMS;

AUTOMATIC MACHINES; CLASSIFICATION TECHNIQUE; DESIGN CONSIDERATIONS; KERNEL CLASSIFICATION; KERNEL CLASSIFIERS; LINEAR CLASSIFICATION; LINEAR CLASSIFIERS; RECENT RESEARCHES;

CLASSIFICATION (OF INFORMATION);

EID: 84991735360     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM TIST, 2(3):27:l-27:27, 2011.
2. Y.-W. Chang, C.-J. Hsieh, K.-W. Chang, M. Ring-gaard, and C.-J. Lin. Training and testing low-degree polynomial data mappings via linear SVM. JMLR, 11:1471-1490, 2010.
3. B.-Y. Chu, C.-H. Ho, C.-H. Tsai, C.-Y. Lin, and C-J. Lin. Warm start for parameter selection of linear classifiers. In KDD, 2015.
4. C. Cortes and V. Vapnik. Support-vector network. MLJ, 20:273-297, 1995.
5. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: a library for large linear classification. JMLR, 9:1871-1874, 2008.
6. M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim. Do we need hundreds of classifiers to solve real world classification problems? JMLR, 15:3133-3181, 2014.
7. S. Fine and K. Scheinberg. Efficient SVM training using low-rank kernel representations. JMLR, 2:243-264, 2001.
8. Q. Gu and J. Han. Clustered support vector machines. In AISTATS, 2013.
9. I. Guyon, S. Gunn, A. B. Hur, and G. Dror. Result analysis of the NIPS 2003 feature selection challenge. In NIPS. 2005.
10. 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.
11. C.-W. Hsu, C.-C. Chang, and C.-J. Lin. A practical guide to support vector classification. Technical report, National Taiwan University, 2003.
12. S. S. Keerthi and C.-J. Lin. Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput, 15(7):1667-1689, 2003.
13. L. Ladicky and P. H. S. Torr. Locally linear support vector machines. In ICML, 2011.
14. Q. Le, T. Sarlos, and A. Smola. Fastfood-approximating kernel expansions in loglinear time. In ICML, 2013.
15. Y.-J. Lee and O. L. Mangasarian. RSVM: Reduced support vector machines. In SDM, 2001.
16. R. A. Lippert and R. M. Rifkin. Infinite-cr limits for Tikhonov regularization. JMLR, 7:855-876, 2006.
17. S. Lloyd. Least squares quantization in PCM. IEEE Trans. Inf. Theor., 28:129-137, 1982.
18. D. Meyer, F. Leisch, and K. Hornik. The support vector machine under test. Neurocomputing, 55:169-186, September 2003.
19. N. Pham and R. Pagh. Fast and scalable polynomial kernels via explicit feature maps. In KDD, 2013.
20. A. Rahimi and B. Recht. Random features for large-scale kernel machines. NIPS, 2008.
21. A. Strehi, J. Ghosh, and R. Mooney. Impact of similarity measures on web-page clustering. In AAAI Workshop on AI for Web Search, 2000.
22. V. Vapnik. Statistical Learning Theory. Wiley, New York, NY, 1998.
23. C. K. I. Williams and M. Seeger. Using the Nyström method to speed up kernel machines. In NIPS, 2001.
24. H.-F. Yu, F.-L. Huang, and C.-J. Lin. Dual coordinate descent methods for logistic regression and maximum entropy models. MLJ, 85:41-75, 2011.
25. G.-X. Yuan, C.-H. Ho, and C.-J. Lin. Recent advances of large-scale linear classification. PIEEE, 100:2584-2603, 2012.
26. K. Zhang, L. Lan, Z. Wang, and F. Moerchen. Scaling up kernel SVM on limited resources: A low-rank linearization approach. In AISTATS, 2012.