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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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5

Optimisation Theory

All of the inductive strategies presented in Chapter 4 have a similar form. The hypothesis function should be chosen to minimise (or maximise) a certain functional. In the case of linear learning machines (LLMs), this amounts to finding a vector of parameters that minimises (or maximises) a certain cost function, typically subject to some constraints. Optimisation theory is the branch of mathematics concerned with characterising the solutions of classes of such problems, and developing effective algorithms for finding them. The machine learning problem has therefore been converted into a form that can be analysed within the framework of optimisation theory.

Depending on the specific cost function and on the nature of ...

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