Information Theory and its Relation to Machine Learning

Abstract

In this position paper, I first describe a new perspective on machine learning (ML) by four basic problems (or levels), namely, "What to learn?", "How to learn?", "What to evaluate?", and "What to adjust?". The paper stresses more on the first level of "What to learn?", or "Learning Target Selection". Towards this primary problem within the four levels, I briefly review the existing studies about the connection between information theoretical learning (ITL [1]) and machine learning. A theorem is given on the relation between the empirically-defined similarity measure and information measures. Finally, a conjecture is proposed for pursuing a unified mathematical interpretation to learning target selection.