First and second order SMO algorithms for LS-SVM classifiers

Neural Processing Letters

Volumn 33, Issue 1, 2011, Pages 31-44

  López, Jorge ^a   Suykens, Johan A K ^b  

^a UNIVERSIDAD AUTÓNOMA DE MADRID   (Spain)

^b Katholieke Universiteit Leuven   (Belgium)

Author keywords

Least squares support vector machines; Sequential minimal optimization; Support vector classification; Working set selection

Indexed keywords

ASYMPTOTIC CONVERGENCE; CONJUGATE GRADIENT ALGORITHMS; FIRST ORDER; HYPERPARAMETERS; LEAST SQUARES SUPPORT VECTOR MACHINES; RATE OF CONVERGENCE; RBF KERNELS; SECOND ORDERS; SELECTION SCHEME; SEQUENTIAL MINIMAL OPTIMIZATION; SMO ALGORITHMS; SUPPORT VECTOR CLASSIFICATION; SVM CLASSIFIERS; WORKING SET SELECTION;

ALGORITHMS; APPROXIMATION THEORY; CLASSIFIERS; CONJUGATE GRADIENT METHOD; CONVERGENCE OF NUMERICAL METHODS; GEARS; OPTIMIZATION; SEQUENTIAL MACHINES; VECTORS;

SUPPORT VECTOR MACHINES;

EID: 79751524883     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-010-9162-9     Document Type: Article

References (21)

1. JAK Suykens J Vandewalle 1999 Least squares support vector machine classifiers Neural Process Lett 9 3 293 300 10.1023/A:1018628609742 1721843
2. C Cortes V Vapnik 1995 Support-vector networks Mach Learn 20 3 273 297 0831.68098
3. SS Keerthi SK Shevade C Bhattacharyya KRK Murthy 2000 A fast iterative nearest point algorithm for support vector machine classifier design IEEE Trans Neural Netw 11 1 124 136 10.1109/72.822516 (Pubitemid 32132894)
4. Suykens JAK, Lukas L, Van Dooren P, De Moor B, Vandewalle J (1999) Least squares support vector machine classifiers: a large scale algorithm. In: Proceedings of the European conference on circuit theory and design (ECCTD), pp 839-842
5. SS Keerthi SK Shevade 2003 SMO algorithm for least-squares SVM formulations Neural Comput 15 2 487 507 1047.68100 10.1162/089976603762553013 (Pubitemid 37049831)
6. Shalev-Shwartz S, Singer Y, Srebro N (2007) Pegasos: primal estimated sub-gradient solver for SVM. In: Proceedings of the 24th international conference on machine learning (ICML), pp 807-814
7. Joachims T (2006) Training linear SVMs in linear time. In: Proceedings of the 12th international conference on knowledge discovery and data mining (SIGKDD), pp 217-226 (Pubitemid 44535518)
8. R-E Fan K-W Chang C-J Hsieh X-R Wang C-J Lin 2008 LIBLINEAR: a library for large linear classification J Mach Learn Res 9 1871 1874
9. Zhou T, Tao D, Wu X (2010) NESVM: a fast gradient method for support vector machines. In: Proceedings of the 12th international conference on data mining (ICDM)
10. R-E Fan P-H Chen C-J Lin 2005 Working set selection using second order information for training support vector machines J Mach Learn Res 6 1889 1918 2249875 (Pubitemid 41798130)
11. Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines software. http://www.csie.ntu.edu.tw/~cjlin/libsvm
12. Platt J-C (1999) Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods: support vector learning. MIT Press, Cambridge, pp 185-208
13. Joachims T (1999) Making large-scale support vector machine learning practical. In: Advances in kernel methods: support vector learning. MIT Press, Cambridge, pp 169-184
14. SS Keerthi SK Shevade C Bhattacharyya KRK Murthy 2001 Improvements to Platt's SMO algorithm for SVM classifier design Neural Comput 13 3 637 649 1085.68629 10.1162/089976601300014493 (Pubitemid 33595014)
15. López J, Barbero Á, Dorronsoro JR (2008) On the equivalence of the SMO and MDM algorithms for SVM training. In: Lecture notes in computer science: machine learning and knowledge discovery in databases, vol 5211. Springer, New York, pp 288-300
16. Lin C-J (2001) Linear convergence of a decomposition method for support vector machines. Technical report
17. P-H Chen R-E Fan C-J Lin 2006 A study on SMO-type decomposition methods for support vector machines IEEE Trans Neural Netw 17 893 908 10.1109/TNN.2006.875973 (Pubitemid 44194150)
18. Rätsch G (2000) Benchmark repository. http://ida.first.fhg.de/ projects/bench/benchmarks.htm
19. T Van Gestel JAK Suykens B Baesens S Viaene J Vanthienen G Dedene B De Moor J Vandewalle 2004 Benchmarking least squares support vector machine classifiers Mach Learn 54 1 5 32 1078.68737 10.1023/B:MACH.0000008082.80494.e0
20. XC Guo JH Yang CG Wu CY Wang YC Liang 2008 A novel LS-SVMs hyper-parameter selection based on particle swarm optimization Neurocomputing 71 16-18 3211 3215 10.1016/j.neucom.2008.04.027
21. Á Barbero J López JR Dorronsoro 2009 Cycle-breaking acceleration of SVM training Neurocomputing 72 7-9 1398 1406 10.1016/j.neucom.2008.12.014