On linear separability of data sets in feature space

Neurocomputing

Volumn 70, Issue 13-15, 2007, Pages 2441-2448

  Chen, Degang ^a   He, Qiang ^b   Wang, Xizhao ^b  

^a NORTH CHINA ELECTRIC POWER UNIVERSITY   (China)

^b HEBEI UNIVERSITY   (China)

Author keywords

Feature space; Kernel; Linear separability; SVM

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA PROCESSING; MAPPING; SELF ORGANIZING MAPS; SUPPORT VECTOR MACHINES;

FEATURE SPACE; KERNEL; LINEAR SEPARABILITY;

NEURAL NETWORKS;

ARTICLE; KERNEL METHOD; LINEAR SYSTEM; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; SUPPORT VECTOR MACHINE;

EID: 34249707478     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2006.12.002     Document Type: Article

References (13)

1. Burges C. A Tutorial on Support Vector Machines for Pattern Recognition. Data Min. Knowl. Discovery 2 2 (1998) 121-167
2. Chen D., He Q., and Wang X. The infinite polynomial kernel for support vector machine. Lect. Notes Artif. Intell. 3584 (2005) 267-275
3. Matthias H., Olivier B., and Bernhard S. Maximal margin classification for metric spaces. J. Comput. Sys. Sci. 71 (2005) 333-359
4. C.A. Micchelli, Algebraic aspects of interpolation, in: Proceedings of Symposia in Applied Mathematics, vol. 36, 1986, pp. 81-102.
5. C. Saunders, M.O. Stitson, J. Weston, L. Bottou, B. Scholkopf, A.J. Smola, Support vector machine reference manual, Technical Report CSD-TR-98-03, Department of Computer Science, Royal Holloway, University of London, Egham, UK, 1998.
6. Schoenberg I.J. Positive definite functions on spheres. Duke Math. J. 9 (1942) 96-108
7. Scholkopf B., and Smola A.J. Learning with Kernels (2002), MIT Press, Cambridge, MA
8. Schurmann J. Pattern Classification: A Unified View of Statistical and Neural Approaches (1996), Wiley, New York
9. A.J. Smola, Regression estimation with support vector learning machines, Diplomarbeit, Technische Universitat Munchen, 1996.
10. Steinwart I. On the influence of the kernel on the consistency of support vector machines. J. Mach. Learn. Res. 2 (2001) 67-93
11. Vapnik V.N. The Nature of Statistical Learning Theory (1995), Springer, New York
12. Vapnik V.N. Statistical Learning Theory (1998), Wiley, New York
13. Vapnik V.N. Preface of Chinese edition of Statistical Learning Theory (2004), Publishing House of Electronics Industry, Beijing