A fixed-center spherical separation algorithm with kernel transformations for classification problems

Computational Management Science

Volumn 6, Issue 3, 2009, Pages 357-372

  Astorino, A ^a   Gaudioso, M ^a  

^a UNIVERSITY OF CALABRIA   (Italy)

Author keywords

Classification; Kernel methods; Separability; Support vector machine

Indexed keywords

EID: 67650177451     PISSN: 1619697X     EISSN: 16196988     Source Type: Journal    
DOI: 10.1007/s10287-007-0051-2     Document Type: Article

References (13)

1. Astorino A, Gaudioso M (2002) Polyhedral separability through successive LP. J OptimTheory Appl 112(2):265-293
2. Astorino A, Gaudioso M (2005) Ellipsoidal separation for classification problems. Optim Methods Softw 20(2-3):261-270
3. Bennett KP, Mangasarian OL (1992) Robust linear programming discrimination of two linearly inseparable sets. Optim Methods Softw 1:23-34
4. Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge
5. Fung G, Mangasarian OL (2001) Proximal support vector machine classifiers, PDF version data mining institute technical report 01-02, February 2001 - Proceedings KDD-2001, San Francisco August 26-29, 2001 - Association for Computing Machinery, New York, pp 77-86
6. Joachims T (1999). Making large-scale support vector machine learning practical. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods: Support vector learning. MIT, Cambridge, pp 169-184
7. Mangasarian OL (1965) Linear and nonlinear separation of patterns by linear programming. Oper Res 13:444-452
8. Murphy PM, Aha DW (1992) UCI repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html
9. Odewahn S, Stockwell E, Pennington R, Humphreys R, Zumach W (1992) Automated star/galaxy discrimination with neural networks. Astron J 103(1):318-331
10. Rosen JB (1965) Pattern separation by convex programming. J Math Anal Appl 10:123-134
11. Schölkopf B, Burges CJC, Smola AJ (eds) (1999) Advances in kernel methods: Support vector learning. MIT, Cambridge
12. Tax DMJ, Duin RPW (1999) Data domain description using support vectors. In: ESANN'1999 proceedings Bruges (Belgium), 21-23 April 1999, D-Facto public, ISBN 2-600049-9-x, pp 251-256
13. Vapnik V (1995) The nature of the statistical learning theory. Springer, New York