Two ellipsoid Support Vector Machines

Expert Systems with Applications

Volumn 41, Issue 18, 2014, Pages 8211-8224

  Czarnecki, Wojciech Marian ^a   Tabor, Jacek ^a  

^a JAGIELLONIAN UNIVERSITY   (Poland)

Author keywords

Classification; Data preprocessing; Mahalanobis distance; Support Vector Machines

Indexed keywords

CLASSIFICATION (OF INFORMATION); OPTIMIZATION; QUALITY CONTROL;

ACTIVITY PREDICTIONS; CLASSIFICATION QUALITY; DATA PREPROCESSING; GENERALIZATION PROPERTIES; GEOMETRICAL STRUCTURE; LINEAR CLASSIFICATION; MAHALANOBIS DISTANCES; OPTIMIZATION PROBLEMS;

SUPPORT VECTOR MACHINES;

EID: 84905823378     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2014.07.015     Document Type: Article

References (33)

1. Bache, K., & Lichman, M. (2013). UCI machine learning repository. URL: .
2. I. Brown, and C. Mues An experimental comparison of classification algorithms for imbalanced credit scoring data sets Expert Systems with Applications 39 3 2012 3446 3453
3. C. Cortes, and V. Vapnik Support-vector networks Machine Learning 20 3 1995 273 297
4. E. Deadman, N.J. Higham, and R. Ralha Blocked schur algorithms for computing the matrix square root Applied parallel and scientific computing 2013 Springer 171 182
5. P. Dhanalakshmi, S. Palanivel, and V. Ramalingam Classification of audio signals using svm and rbfnn Expert Systems with Applications 36 3 2009 6069 6075
6. Do, H., Kalousis, A., Wang, J., & Woznica, A. (2012). A metric learning perspective of svm: On the relation of lmnn and svm. In Proceedings of international conference on artificial intelligence and statistics (pp. 308-317).
7. G. Fung, and O.L. Mangasarian Proximal support vector machine classifiers Proceedings of the knowledge discovery and data mining conference 2001 Association for Computing Machinery New York 77 86
8. K. Huang, H. Yang, I. King, and M.R. Lyu Maxi-min margin machine: Learning large margin classifiers locally and globally IEEE Transactions on Neural Networks 19 2 2008 260 272 (Pubitemid 351359291)
9. Jayadeva, R. Khemchandani, and S. Chandra Twin support vector machines for pattern classification IEEE Transactions on Pattern Analysis and Machine Intelligence 29 5 2007 905 910 (Pubitemid 46534787)
10. T. Le, D. Tran, T. Hoang, W. Ma, and D. Sharma Generalised support vector machine for brain-computer interface B.-L. Lu, L. Zhang, J. Kwok, Neural information processing Lecture notes in computer science Vol. 7062 2011 Springer Berlin Heidelberg 692 700
11. Mahalanobis, P. C. (1936). On the generalised distance in statistics. In Proceedings national institute of science, India (Vol. 2, pp. 49-55).
12. M. Momma, K. Hatano, and H. Nakayama Ellipsoidal support vector machines Journal of Machine Learning Research 13 2010 31 46
13. R. Moraes, J.F. Valiati, and W.P. GaviãO Neto Document-level sentiment classification: An empirical comparison between svm and ann Expert Systems with Applications 40 2 2013 621 633
14. T.E. Oliphant Python for scientific computing Computing in Science & Engineering 9 3 2007 10 20 (Pubitemid 46646860)
15. E. Owusu, Y. Zhan, and Q.R. Mao A neural-adaboost based facial expression recognition system Expert Systems with Applications 41 7 2014 3383 3390
16. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, and O. Grisel et al. Scikit-learn: Machine learning in Python Journal of Machine Learning Research 12 2011 2825 2830
17. X. Peng, and D. Xu Twin mahalanobis distance-based support vector machines for pattern recognition Information Science 200 2012 22 37
18. I.T. Podolak Hierarchical classifier with overlapping class groups Expert Systems with Applications 34 1 2008 673 682 (Pubitemid 46756293)
19. M. Rushdi Saleh, M.T. Martín-Valdivia, A. Montejo-Ráez, and L. Ureña-López Experiments with svm to classify opinions in different domains Expert Systems with Applications 38 12 2011 14799 14804
20. N. Saravanan, V. Kumar Siddabattuni, and K. Ramachandran A comparative study on classification of features by svm and psvm extracted using morlet wavelet for fault diagnosis of spur bevel gear box Expert Systems with Applications 35 3 2008 1351 1366
21. M. Sastry, J.F. Lowrie, S.L. Dixon, and W. Sherman Large-scale systematic analysis of 2d fingerprint methods and parameters to improve virtual screening enrichments Journal of Chemical Information and Modeling 50 5 2010 771 784
22. P.K. Shivaswamy, and T. Jebara Ellipsoidal machines Journal of Machine Learning Research - Proceedings Track 2 2007 484 491
23. S. Smusz, R. Kurczab, and A.J. Bojarski A multidimensional analysis of machine learning methods performance in the classification of bioactive compounds Chemometrics and Intelligent Laboratory Systems 128 2013 89 100
24. V. Strassen Gaussian elimination is not optimal Numerische Mathematik 13 4 1969 354 356
25. M.E. Tipping Sparse bayesian learning and the relevance vector machine Journal of Machine Learning Research 1 2001 211 244 (Pubitemid 33687203)
26. E.D. Übeyli Multiclass support vector machines for diagnosis of erythemato-squamous diseases Expert Systems with Applications 35 4 2008 1733 1740
27. K.Q. Weinberger, and L.K. Saul Distance metric learning for large margin nearest neighbor classification Journal of Machine Learning Research 10 2009 207 244
28. Williams, V. V. (2011). Breaking the coppersmith-winograd barrier. unpublished manuscript.
29. M.A. Woodbury Inverting modified matrices Statistical research group memorandum reports 42 1950 Princeton University Princeton, NJ
30. C.W. Yap Padel-descriptor: An open source software to calculate molecular descriptors and fingerprints Journal of Computational Chemistry 32 7 2011 1466 1474
31. T. Zhang Statistical analysis of some multi-category large margin classification methods Journal of Machine Learning Research 5 2004 1225 1251
32. L. Zhou, K.K. Lai, and L. Yu Least squares support vector machines ensemble models for credit scoring Expert Systems with Applications 37 1 2010 127 133
33. Zhu, X., Gong, P., Zhao, Z., & Zhang, C. (2012). Learning similarity metric with svm. In Proceedings of The 2012 international joint conference on neural networks.