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Abstract

Kernel methods have become very popular in machine learning research and many fields of applications. This tutorial will introduce kernels, their basic properties and methods which take advantage of them. We will use real world problems from computational biology and beyond as examples to illustrate how do select and engineer an appropriate kernel function. This tutorial will begin with a presentation of kernel methods and their properties. This will be followed by an introduction to the theory of support vector algorithms such as support vector machines, support vector regression and kernel principal component analysis. We will also briefly discuss optimization techniques to obtain solutions and discuss variations such as v-SVMs or C-SVMs. We will also discuss how kernel methods can be used for structured output prediction and nonparametric statistical inference. In the last part, we will show how kernel methods can be applied to problems in computational biology.