Non-parallel support vector classifiers with different loss functions

Neurocomputing

Volumn 143, Issue , 2014, Pages 294-301

  Mehrkanoon, Siamak ^a   Huang, Xiaolin ^a   Suykens, Johan A K ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Hinge loss; Kernel trick; Least squares loss; Non parallel classifiers; Pinball loss

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS;

GENERALIZED EIGENVALUES; KERNEL TRICK; LEAST SQUARE; LEAST SQUARES VERSIONS; PROXIMAL SUPPORT VECTOR MACHINES; REGULARIZATION TERMS; SUPPORT VECTOR CLASSIFIERS; TWIN SUPPORT VECTOR MACHINES;

SUPPORT VECTOR MACHINES;

ACCURACY; ARTICLE; CONCEPTUAL FRAMEWORK; HINGE LOSS; KERNEL METHOD; LEARNING ALGORITHM; LEAST SQUARE LOSS; LOSS FUNCTION; MATHEMATICAL COMPUTING; MISCLASSIFICATION LOSS; NON PARALLEL SUPPORT VECTOR MACHINE; PINBALL LOSS; PRIORITY JOURNAL; PROBLEM IDENTIFICATION; PROCESS OPTIMIZATION; SCATTER LOSS; STATISTICAL CONCEPTS; SUPPORT VECTOR MACHINE; THEORY VALIDATION;

EID: 84904806309     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2014.05.063     Document Type: Article

References (30)

1. Vapnik V. Statistic Learning Theory 1998, Cambridge University Press, New York.
2. Schölkopf B., Smola A.J. Learning with Kernels 2002, The MIT Press, Cambridge, Massachusetts.
3. J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Signapore, 2002.
4. Suykens J.A.K., Vandewalle J. Least squares support vector machine classifiers. Neural Process. Lett. 1999, 9(3):293-300.
5. Mangasarian O.L., Wild E.W. Multisurface proximal support vector machine classification via generalized eigenvalues. IEEE Trans. Pattern Anal. Mach. Intell. 2006, 28(1):69-74.
6. Zhao Y.-P., Zhao J., Zhao M. Twin least squares support vector regression. Neurocomputing 2013, 118:225-236.
7. Peng X. Efficient twin parametric insensitive support vector regression model. Neurocomputing 2012, 79:26-38.
8. Peng X., Xu D. Bi-density twin support vector machines for pattern recognition. Neurocomputing 2013, 99:134-143.
9. Shao Y., Chen W., Huang W., Yang Z., Deng N. The best separating decision tree twin support vector machine for multi-class classification. Procedia Comput. Sci. 2013, 17:1032-1038.
10. Shao Y., Deng N., Yang Z. Least squares recursive projection twin support vector machine for classification. Pattern Recognit. 2012, 45(6):2299-2307.
11. Yang Z., He J., Shao Y. Feature selection based on linear twin support vector machines. Procedia Comput. Sci. 2013, 17:1039-1046.
12. Jayadeva, Khemchandani R., Chandra S. Twin support vector machines for pattern classification. IEEE Trans. Pattern Anal. Mach. Intell. 2007, 29(5):905-910.
13. Shao Y., Zhang C., Wang X., Deng N. Improvements on twin support vector machines. IEEE Trans. Neural Netw. 2011, 22(6):962-968.
14. G. Fung, O.L. Mangasarian, Proximal support vector machine classifiers, in: Proceedings of the 7-th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 77-86.
15. Kumar M., Gopal M. Least squares twin support vector machines for pattern classification. Expert Syst. Appl. 2009, 36(4):7535-7543.
16. J.C. Platt, Fast training of support vector machines using sequential minimal optimization, in: Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge, Massachusetts, 1999, pp. 185-208.
17. Fan R.E., Chen P.H., Lin C.J. Working set selection using second order information for training support vector machines. J. Mach. Learn. Res. 2005, 6:1889-1918.
18. Koenker R. Quantile Regression 2005, Cambridge University Press, New York.
19. Steinwart I., Christmann A. How SVMs can estimate quantiles and the median. Adv. Neural Inf. Process. Syst. 2008, 20:305-312.
20. Steinwart I., Christmann A. Estimating conditional quantiles with the help of the pinball loss. Bernoulli 2011, 17(1):211-225.
21. Huang X., Shi L., Suykens J.A.K. Support vector machine classifier with pinball loss. IEEE Trans. Pattern Anal. Mach. Intell. 2014, 36(5):984-997.
22. A. Frank, A. Asuncion, UCI Machine Learning Repository, 2010.
23. Joshi S., Ramakrishnan G., Chandra S. Using sequential unconstrained minimization techniques to simplify SVM solvers. Neurocomputing 2012, 77(1):253-260.
24. Xavier de Souza S., Suykens J.A.K., Vandewalle J., Bollé D. Coupled simulated annealing. IEEE Trans. Syst. Man Cybern. Part B 2010, 40(2):320-335.
25. Li S., Tan M. Tuning SVM parameters by using a hybrid CLPSO-BFGS algorithm. Neurocomputing 2010, 73:2089-2096.
26. Bao Y., Hu Z., Xiong T. A PSO and pattern search based memetic algorithm for SVMs parameters optimization. Neurocomputing 2013, 117:98-106.
27. Wang X., Niu Y. New one-versus-all ν-SVM solving intra-inter class imbalance with extended manifold regularization and localized relative maximum margin. Neurocomputing 2013, 115:106-121.
28. Bao Y., Xiong T., Hu Z. Multi-step-ahead time series prediction using multiple-output support vector regression. Neurocomputing 2014, 129:482-493.
29. Xiong T., Bao Y., Hu Z. Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting. Knowl.-Based Syst. 2014, 55:87-100.
30. Xiong T., Bao Y., Hu Z. Does restraining end effect matter in EMD-based modeling framework for time series prediction? Some experimental evidences. Neurocomputing 2014, 123:174-184.