# Learning Theory and Support Vector Machines - a primer

## TL;DR

This paper introduces the fundamentals of statistical learning theory, emphasizing the concepts of empirical and structural risk minimization, and discusses the Support Vector Machine as a key implementation.

## Contribution

It provides a concise overview of learning theory principles and explains the theoretical basis of Support Vector Machines for decision making.

## Key findings

- Clarifies the distinction between empirical and structural risk minimization
- Explains the theoretical foundation of Support Vector Machines
- Highlights the importance of statistical learning theory in model construction

## Abstract

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.