Inductive Confidence Machines for Regression
Purchase on Springer.com
$29.95 / €24.95 / £19.95*
* Final gross prices may vary according to local VAT.
The existing methods of predicting with confidence give good accuracy and confidence values, but quite often are computationally inefficient. Some partial solutions have been suggested in the past. Both the original method and these solutions were based on transductive inference. In this paper we make a radical step of replacing transductive inference with inductive inference and define what we call the Inductive Confidence Machine (ICM); our main concern in this paper is the use of ICM in regression problems. The algorithm proposed in this paper is based on the Ridge Regression procedure (which is usually used for outputting bare predictions) and is much faster than the existing transductive techniques. The inductive approach described in this paper may be the only option available when dealing with large data sets.
- Cristianini, N., & Shawe-Taylor, J. (2000). Support Vector Machines and Other Kernel-based Learning Methods. Cambridge: Cambridge University Press.
- Fraser, D. A. S. (1957). Non-parametric Methods in Statistics. New York: Wiley.
- Gammerman, A., Vapnik, V., & Vovk, V. (1998). Learning by transduction. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (pp. 148–156). San Francisco: Morgan Kaufmann.
- Li, M., & Vitányi, P. (1997). An Introduction to Kolmogorov Complexity and Its Applications. Second edition. New York: Springer.
- Melluish, T., Saunders, C., Nouretdinov, I., & Vovk, V. (2001). Comparing the Bayes and typicalness frameworks. ECML’01.
- Melluish, T., Vovk, V., & Gammerman, A. (1999). Transduction for Regression Estimation with Confidence. NIPS’99.
- Nouretdinov, I., Melluish, T., & Vovk, V. (1999). Ridge Regression Confidence Machine. Proceedings of the 18th International Conference on Machine Learning.
- Nouretdinov, I., Vovk, V., V'yugin, V., & Gammerman, A. (2001). Transductive Confidence Machine is universal. Work in progress.
- Proedrou, K., Nouretdinov, I., Vovk, V., & Gammerman, A. (2001). Transductive Confidence Machines for Pattern Recognition. Proceedings of the 13th European Conference on Machine Learning.
- Saunders, C., Gammerman, A., & Vovk, V. (1999). Transduction with confidence and credibility. Proceedings of the 16th International Joint Conference on Artificial Intelligence (pp. 722–726).
- Saunders, C., Gammerman, A., & Vovk, V. (2000). Computationally efficient transductive machines. ALT’00 Proceedings.
- Vapnik, V. (1998). Statistical Learning Theory. New York: Wiley.
- Vovk, V., Gammerman, A., & Saunders, C. (1999). Machine-learning applications of algorithmic randomness. Proceedings of the 16th International Conference on Machine Learning (pp. 444–453).
- Vovk, V., & Gammerman, A. (2001). Algorithmic Theory of Randomness and its Computer Applications. Manuscript.
- Vovk, V., and Gammerman, A. (1999). Statistical applications of algorithmic randomness. Bulletin of the International Statistical Institute. The 52nd Session. Contributed Papers. Tome LVIII. Book 3 (pp. 469–470).
- Inductive Confidence Machines for Regression
- Book Title
- Machine Learning: ECML 2002
- Book Subtitle
- 13th European Conference on Machine Learning Helsinki, Finland, August 19–23, 2002 Proceedings
- pp 345-356
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- Additional Links
- Industry Sectors
- eBook Packages
- Editor Affiliations
- 1. Department of Computer Science, University of Helsinki
- Author Affiliations
- 4. Department of Computer Science, Royal Holloway, University of London, TW20 0EX, Egham, Surrey, England
To view the rest of this content please follow the download PDF link above.