Kernel methods: A paradigm for pattern analysis

Kernel Methods in Bioengineering, Signal and Image Processing

Volumn , Issue , 2006, Pages 1-39

  Cristianini, Nello ^a   Shawe Taylor, John ^b   Saunders, Craig ^c  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

^b UNIVERSITY OF LONDON   (United Kingdom)

^c UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84900587559     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-59904-042-4.ch001     Document Type: Chapter

References (16)

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3. Cortes, C., Haffner, P., & Mohri, M. (2002). Rational kernels.
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5. De Bie, T., & Cristianini, N. (2004). Kernel methods for exploratory data analysis: A demonstration on text data. In Proceedings of the Joint IAPR International Workshops on Syntactical and Structural Pattern Recognition (SSPR 2004) and Statistical Pattern Recognition (SPR 2004).
6. De Bie, T., Cristianini, N., & Rosipal, R. (2004). Eigenproblems in pattern recognition. In E. Bayro-Corrochano (Ed.), Handbook of computational geometry for pattern recognition, computer vision, neurocomputing and robotics. Heidelberg, Germany: Springer-Verlag.
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9. Kashima, H., & Inokuchi, A. (2002). Kernels for graph classification.
10. th Conference on Learning Theory (COLT).
11. Lanckriet, G., Cristianini, N., Bartlett, P., Ghaoui, L. E., & Jordan, M. I. (2004). Learning the kernel matrix with semidefinite programming. Journal of Machine Learning Research, 5, 27-72.
12. Lodhi, H., Saunders, C., Shawe-Taylor, J., Cristianini, N., & Watkins, C. (2002). Text classification using string kernels. Journal of Machine Learning Research, 2, 419-444.
13. th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 3201 (pp. 384-399).
14. Schoelkopf, B., & Smola, A. (2002). Learning with kernels. Cambridge, MA: MIT Press.
15. Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. Cambridge University Press.
16. Vapnik, V. (1998). Statistical learning theory. New York: Wiley.