A Preference Optimization Based Unifying Framework for Supervised Learning Problems

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 a general 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.