B5: Introduction to Kernel Methods F Jäkel |
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University of Osnabrück, Germany |
| The abilities to learn and to categorize are fundamental for cognitive systems, be it animals or machines, and therefore have attracted attention from engineers and psychologists alike. Early machine learning algorithms were inspired by psychological and neural models of learning. However, machine learning is now an independent and mature field that has moved beyond psychologically or neurally inspired algorithms towards providing foundations for a theory of learning that is rooted in statistics. Here, we provide an introduction to a popular class of machine learning tools, called kernel methods. These methods are widely used in computer vision and modern data analysis. They are therefore potentially interesting for vision research, too. However, reading about kernel methods can sometimes be intimidating because many papers in machine learning assume that the reader is familiar with functional analysis. In this tutorial, I give basic explanations of the key theoretical concepts that are necessary to be able to get started with kernel methods - the so-called kernel trick, positive definite kernels, reproducing kernel Hilbert spaces, the representer theorem, and regularization. |
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