Bayesian hierarchical ordinal regression

Authors:
Ulrich Paquet;Sean Holden;Andrew Naish-Guzman
Affiliations:
Computer Laboratory, University of Cambridge, Cambridge, UK;Computer Laboratory, University of Cambridge, Cambridge, UK;Computer Laboratory, University of Cambridge, Cambridge, UK
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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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.