Supervised Learning as Preference Optimization: A General Framework and its Applications

Supervised learning is characterized by a broad spectrum of learning problems, often involving structured predictions, including classification and regressions problems, 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 paper, we show that the 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 can be effectively used to address a variety of real-world applications are reported showing the flexibility and effectiveness of the approach.