Unformatted text preview: the sample size is just a few thousand points. Such considerations have driven the design of specific algorithms for Support Vector Machines that can exploit the sparseness of the solution, the convexity of the optimisation problem, and the implicit mapping into feature space. All of these features help to create remarkable computational efficiency. The elegant mathematical characterisation of the solutions can be further exploited to provide stopping criteria and decomposition procedures for very large datasets. In this chapter we will briefly review some of the most common approaches before describing in detail one particular algorithm, Sequential Minimal Optimisation (SMO), that has the additional advantage of not only being one of the most competitive but also being simple to implement. As an exhaustive discussion of optimisation algorithms is not possible here, a number of pointers to relevant literature and on-line software is provided in...
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- Spring '11