A Preference Optimization Based Unifying Framework for Supervised Learning Problems
Inhaltsverzeichnis
Reference
Fabio Aiolli, Alessandro Sperduti: A Preference Optimization Based Unifying Framework for Supervised Learning Problems. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 19-42.
DOI
http://dx.doi.org/10.1007/978-3-642-14125-6_2
Abstract
Supervised learning is characterized by a broad spectrum of learning problems, often involving structured types of prediction, including classification, ranking-based predictions (label and instance ranking), and (ordinal) regression in its various forms. All these different learning problems are typically addressed by specific algorithmic solutions.
In this chapter, we propose ageneral preference learning model (GPLM), which gives an easy way to translate any supervised learning problem and the associated cost functions into sets of preferences to learn from. A large margin principled approach to solve this problem is also proposed.
Examples of how the proposed framework has been effectively used by us to address non-standard real-world applications are reported showing the flexibility and effectiveness of the approach.
Extended Abstract
Bibtex
@incollection{
year={2011},
isbn={978-3-642-14124-9},
booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
doi={10.1007/978-3-642-14125-6_2},
title={A Preference Optimization Based Unifying Framework for Supervised Learning Problems},
url={http://dx.doi.org/10.1007/978-3-642-14125-6_2, http://de.evo-art.org/index.php?title=A_Preference_Optimization_Based_Unifying_Framework_for_Supervised_Learning_Problems },
publisher={Springer Berlin Heidelberg},
author={Aiolli, Fabio and Sperduti, Alessandro},
pages={19-42},
language={English}
}
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