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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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3

Kernel–Induced Feature Spaces

The limited computational power of linear learning machines was highlighted in the 1960s by Minsky and Papert. In general, complex real-world applications require more expressive hypothesis spaces than linear functions. Another way of viewing this problem is that frequently the target concept cannot be expressed as a simple linear combination of the given attributes, but in general requires that more abstract features of the data be exploited. Multiple layers of thresholded linear functions were proposed as a solution to this problem, and this approach led to the development of multi-layer neural networks and learning algorithms such as back-propagation for training such systems.

Kernel representations offer ...

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