Qualified probabilistic predictions using graphical models

Authors:
Zhiyuan Luo;Alex Gammerman
Affiliations:
Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey, UK;Computer Learning Research Centre, Royal Holloway, University of London, Egham, Surrey, UK
Venue:
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2005

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Abstract

We consider probabilistic predictions using graphical models and describe a newly developed method, fully conditional Venn predictor (FCVP). FCVP can provide upper and lower bounds for the conditional probability associated with each predicted label. Empirical results confirm that FCVP can give well-calibrated predictions in online learning mode. Experimental results also show the prediction performance of FCVP is good in both the online and the offline learning setting without making any additional assumptions, apart from i.i.d.