A Kernel Between Sets of Vectors

Proceedings, Twentieth International Conference on Machine Learning

Volumn 1, Issue , 2003, Pages 361-368

  Kondor, Risi ^a   Jebara, Tony ^a  

^a Department of Biomedical Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN PROCESSES; KERNEL FUNCTION; KERNEL METHODS;

ALGORITHMS; COLOR IMAGE PROCESSING; DATA ACQUISITION; GRAPH THEORY; MATHEMATICAL TRANSFORMATIONS; PROBABILITY DENSITY FUNCTION; STATISTICAL METHODS; TIME SERIES ANALYSIS; VECTORS;

LEARNING SYSTEMS;

EID: 1942452419     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Bhattacharyya, A. (1943). On a measure of divergence between two statistical populations defined by their probability distributions. Bull. Calcutta Math Soc., 35, 99-110.
2. Collins, M., & Duffy, N. (2002). Convolution kernels for natural language. Advances in Neural Information Processing Systems 14 (pp. 625-632). Cambridge, MA: MIT Press.
3. Cortes, C., Haffner, P., & Mohri, M. (2003). Rational kernels. Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press.
4. Dirac, P. A. M. (1930). The principles of quantum mechanics. Oxford University Press.
5. Haussler, D. (1999). Convolution kernels on discrete structures (Technical Report UCSC-CRL-99-10). Department of Computer Science, University of California at Santa Cruz.
6. Jaakkola, T., & Haussler, D. (1999). Exploiting generative models in discriminative classifiers. Advances in Neural Information Processing Systems 11. Cambridge, MA: MIT Press.
7. Jebara, T. (2003). Convex invariance learning. Ninth International Workshop on Artificial Intelligence and Statistics.
8. Jebara, T., & Kondor, R. (2003). Bhattacharyya and expected likelihood kernels. Proceedings of the Sixteenth Annual Conference on Learning Theory and Seventh Kernel Workshop. In press.
9. Kondor, R., & Lafferty, J. (2002). Diffusion kernels on graphs and other discrete input spaces. Machine Learning: Proceedings of the Nineteenth International Conference (ICML '02).
10. Lafferty, J., & Lebanon, G. (2003). Information diffusion kernels. Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press.
11. Leslie, C., Eskin, E., Weston, J., & Noble, W. S. (2003). Mismatch string kernels for SVM protein classifiaction. Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press.
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. Mackay, D. J. C. (1997). Gaussian processes: A replacement for neural networks? Tutorial at the Tenth Annual Conference on Neural Information Processing Systems. Available from http://wol.ra.phy.cam.ac.uk/pub/mackay/.
14. Schölkopf, B., & Smola, A. J. (2001). Learning with kernels: Support vector machines, regularization, optimization and beyond. Cambridge, MA: MIT Press.
15. Schölkopf, B., Smola, A. J., & Müller, K.-R. (1998). Nonlinear principal component analysis as a kernel eigenvalue problem. Neural Computation, 10, 1299-1319.
16. Vishwanathan, S. V. N., & Smola, A. J. (2003). Fast kernels for string and tree matching. Advances in Neural Information Processing Systems 15. Cambridge, MA: MIT Press.
17. Watkins, C. (2000). Dynamic alignment kernels. In A. J. Smola, B. Schölkopf, P. Bartlett, and D. Schuurmans (Eds.), Advances in kernel methods. Cambridge, MA: MIT Press.
18. Wolf, L., & Shashua, A. (2003). Kernel principal angles for classification machines with applications to image sequence interpretation. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR).
19. Zhu, H., Williams, C. K. I., Rohwer, R., & Morciniec, M. (1997). Gaussian regression and optimal finite dimensional linear models (Technical Report NCRG/97/011). Aston University, Neural Computing Research Group.