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An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini, John Shawe-Taylor

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7

Implementation Techniques

In the previous chapter we showed how the training of a Support Vector Machine can be reduced to maximising a convex quadratic form subject to linear constraints. Such convex quadratic programmes have no local maxima and their solution can always be found efficiently. Furthermore this dual representation of the problem showed how the training could be successfully effected even in very high dimensional feature spaces. The problem of minimising differentiable functions of many variables has been widely studied, especially in the convex case, and most of the standard approaches can be directly applied to SVM training. However, in many cases specific techniques have been developed to exploit particular features of ...

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