In this chapter we illustrate the use of the algorithms described in this book, by examining some interesting applications. Support Vector Machines have been applied to many real-world problems, so that the material in this chapter is by no means exhaustive. The role of this final chapter is to show how the approach can be used successfully in very diverse fields, and how the necessary design choices can be made in each case.

Applying the Support Vector approach to a particular practical problem involves resolving a number of design questions. We do not dwell on the question of precise specification of the input domain, generation of the training data, and so on. We treat these as given, though in practice they can frequently be refined through an interaction between the SVM system designer and the domain practitioner.

Such a collaboration may also be required to resolve the first design problem, that of choosing an appropriate kernel for the given application. There are standard choices such as a Gaussian or polynomial kernel that are the default options, but if these prove ineffective or if the inputs are discrete structures more elaborate kernels will be needed. By implicitly defining a feature space, the kernel provides the description language used by the machine for viewing the data. Frequently, the designer can work with the more intuitive notion of similarity in the input space and this is where domain experts can give invaluable assistance in helping to formulate appropriate similarity measures. […]