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 .
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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
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DOI: https://doi.org/10.1007/11550907_42
Publisher Name: Springer, Berlin, Heidelberg
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