Statistical Learning Theory

Abstract

This chapter starts by describing the necessary concepts and assumptions to ensure supervised learning. Later on, it details the Empirical Risk Minimization (ERM) principle, which is the key point for the Statistical Learning Theory (SLT). The ERM principle provides upper bounds to make the empirical risk a good estimator for the expected risk, given the bias of some learning algorithm. This bound is the main theoretical tool to provide learning guarantees for classification tasks. Afterwards, other useful tools and concepts are introduced.