Learning Theory and Support Vector Machines - a primer
It provides a primer on foundational concepts for learners in machine learning, but is incremental as it offers an introduction rather than new research.
The paper introduces the fundamentals of statistical learning theory, focusing on empirical vs. structural risk minimization and its implementation in Support Vector Machines, without presenting new results or numbers.
The main goal of statistical learning theory is to provide a fundamental framework for the problem of decision making and model construction based on sets of data. Here, we present a brief introduction to the fundamentals of statistical learning theory, in particular the difference between empirical and structural risk minimization, including one of its most prominent implementations, i.e. the Support Vector Machine.