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Abstract: In this paper, we propose a probabilistic kernel approach to preference learning based on Gaussian processes. A new likelihood function is proposed to capture the preference relations in the Bayesian framework. The generalized formulation is also applicable to tackle many multiclass problems. The overall approach has the advantages of Bayesian methods for model selection and probabilistic prediction. Experimental results compared against the constraint classification approach on... (Update)
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BibTeX entry: (Update)
@misc{ chu-preference,
author = "Wei Chu and Zoubin Ghahramani",
title = "Preference Learning with Gaussian Processes",
url = "citeseer.ist.psu.edu/chu05preference.html" }
Citations (may not include all citations):269 Bayesian learning for neural networks (context) - Neal - 1996
78 Gaussian processes for regression - Williams, Rasmussen - 1996 DBLP
72 Bow: A toolkit for statistical language modeling (context) - McCallum - 1996
22 Bayesian methods for backpropagation networks (context) - MacKay - 1994
8 Probability estimates for multi-class classification by pair.. - Wu, Lin et al. - 2004 ACM
5 Learning preference relations for information retrieval - Herbrich, Graepel et al. - 1998
4 Log-linear models for label ranking - Dekel, Keshet et al. - 2004
4 Pairwise preference learning and ranking (context) - Furnkranz, Hullermeier - 2003
3 Learning subjective functions with large margins - Fiechter, Rogers - 2000 DBLP
3 Sparse online Gaussian processes (context) - Csato, Opper - 2002
1 Preference learning (context) - Furnkranz, Hullermeier - 2005
1 Prospects of preferences (context) - Doyle - 2004
1 Learning with kernels (context) - in, Springer et al. - 2002 ACM
1 del Coz (context) - Bahamonde, Bayon et al. - 2004
1 Learning preferences for multiclass problems - Aiolli, Sperduti - 2004
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