Supervised learning as preference optimization

Learning with preferences is receiving more and more attention in the last few years. The goal in this setting is to learn based on qualitative or quantitative declared preferences between objects of a domain. In this paper we give a survey of a recent framework for supervised learning based on preference optimization. In fact, many of the broad set of supervised tasks can all be seen as particular instances of this preference based framework. They include simple binary classification, (single or multi) label multiclass classification, ranking problems, and (ordinal) regression, just to name a few. We show that the proposed general preference learning model (GPLM), which is based on a large-margin principled approach, gives a flexible way to codify cost functions for all the above problems as sets of linear preferences. Examples of how the proposed framework has been effectively used to address a variety of real-world applications are reported clearly showing the flexibility and effectiveness of the approach.