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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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6

Support Vector Machines

The material covered in the first five chapters has given us the foundation on which to introduce Support Vector Machines, the learning approach originally developed by Vapnik and co-workers. Support Vector Machines are a system for efficiently training the linear learning machines introduced in Chapter 2 in the kernel-induced feature spaces described in Chapter 3, while respecting the insights provided by the generalisation theory of Chapter 4, and exploiting the optimisation theory of Chapter 5. An important feature of these systems is that, while enforcing the learning biases suggested by the generalisation theory, they also produce ‘sparse’ dual representations of the hypothesis, resulting in extremely efficient ...

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