Sparse reductions for fixed-size least squares support vector machines on large scale data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7818 LNAI, Issue PART 1, 2013, Pages 161-173

  Mall, Raghvendra ^a   Suykens, Johan A K ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ITERATIVE APPROXIMATIONS; LARGE SCALE CLASSIFICATIONS; LARGE-SCALE DATASETS; LEAST SQUARES SUPPORT VECTOR MACHINES; MINIMUM SQUARED ERROR; REAL-WORLD DATASETS; REGRESSION PROBLEM; THRESHOLD METHODS;

DATA MINING;

ITERATIVE METHODS;

EID: 84893618270     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-37453-1_14     Document Type: Conference Paper

References (18)

1. Hoegaerts, L., Suykens, J.A.K., Vandewalle, J., De Moor, B.: A comparison of pruning algorithms for sparse least squares support vector machines. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 1247-1253. Springer, Heidelberg (2004) (Pubitemid 39750897)
2. Geebelen, D., Suykens, J.A.K., Vandewalle, J.: Reducing the Number of Support Vectors of SVM classifiers using the Smoothed Seperable Case Approximation. IEEE Transactions on Neural Networks and Learning Systems 23(4), 682-688 (2012)
3. Suykens, J.A.K., Vandewalle, J.: Least Squares Support Vector Machine Classifiers. Neural Processing Letters 9(3), 293-300 (1999)
4. Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer (1995)
5. Suykens, J.A.K., Lukas, L., Vandewalle, J.: Sparse approximation using Least Squares Support Vector Machines. In: Proceedings of IEEE International Symposium on Circuits and Systems (ISCAS 2000), pp. 757-760 (2000)
6. Li, Y., Lin, C., Zhang, W.: Improved Sparse Least-Squares Support Vector Machine Classifiers. Neurocomputing 69(13), 1655-1658 (2006)
7. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific Publishing Co., Pte., Ltd., Singapore (2002)
8. Nystr̈om, E.J.: Über die praktische Auflösung von Integralgleichungen mit Anwendungen auf Randwertaufgaben. Acta Mathematica 54, 185-204 (1930)
9. Baker, C.T.H.: The Numerical Treatment of Integral Equations. Oxford Claredon Press (1983)
10. De Brabanter, K., De Brabanter, J., Suykens, J.A.K., De Moor, B.: Optimized Fixed-Size Kernel Models for Large Data Sets. Computational Statistics & Data Analysis 54(6), 1484-1504 (2010)
11. Karsmakers, P., Pelckmans, K., De Brabanter, K., Hamme, H.V., Suykens, J.A.K.: Sparse conjugate directions pursuit with application to fixed-size kernel methods. Machine Learning, Special Issue on Model Selection and Optimization in Machine Learning 85(1), 109-148 (2011)
12. Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.: Use of the Zero Norm with Linear Models and Kernel Methods. Journal of Machine Learning Research 3, 1439-1461 (2003)
13. Huang, K., Zheng, D., Sun, J., et al.: Sparse Learning for Support Vector Classification. Pattern Recognition Letters 31(13), 1944-1951 (2010)
14. 0-norm minimization. In: ESANN 2011, pp. 189-194 (2011)
15. Blake, C.L., Merz, C.J.: UCI repository of machine learning databases, Irvine, CA (1998), http://archive.ics.uci.edu/ml/datasets.html
16. Williams, C.K.I., Seeger, M.: Using the Nyström method to speed up kernel machines. In: Advances in Neural Information Processing Systems, vol. 13, pp. 682-688 (2001)
17. Scott, D.W., Sain, S.R.: Multi-dimensional Density Estimation. Data Mining and Computational Statistics 23, 229-263 (2004)
18. Xavier de Souza, S., Suykens, J.A.K., Vandewalle, J., Bolle, D.: Coupled Simulated Annealing for Continuous Global Optimization. IEEE Transactions on Systems, Man, and Cybertics - Part B 40(2), 320-335 (2010)