Abstract
We present a Bayesian approach to ordinal regression. Our model is based on a hierarchical mixture of experts model and performs a soft partitioning of the input space into different ranks, such that the order of the ranks is preserved. Experimental results on benchmark data sets show a comparable performance to support vector machine and Gaussian process methods.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chu, W., Ghahramani, Z.: Gaussian processes for ordinal regression. Technical report, Gatsby Computational Neuroscience Unit, University College London (2004)
Jordan, M.I., Jacobs, R.A.: Hierarchical mixtures of experts and the EM algorithm. Neural Computation 6, 181–214 (1994)
MacKay, D.J.C.: The evidence framework applied to classification networks. Neural Computation 4(5), 698–714 (1992)
Shashua, A., Levin, A.: Ranking with large margin principle: two approaches. In: Advances in Neural Information Processing Systems, vol. 15, pp. 937–944. MIT Press, Cambridge (2003)
Waterhouse, S.R., MacKay, D.J.C., Robinson, A.J.: Bayesian methods for mixtures of experts. In: Advances in Neural Information Processing Systems, vol. 8, pp. 351–357. MIT Press, Cambridge (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Paquet, U., Holden, S., Naish-Guzman, A. (2005). Bayesian Hierarchical Ordinal Regression. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_42
Download citation
DOI: https://doi.org/10.1007/11550907_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)