Prior knowledge for pattern recognition: Difference between revisions

Revision as of 14:28, 7 September 2006

Revision as of 18:00, 24 October 2006

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== Definition ==

Prior knowledge, as defined in [Scholkopf02], refers to all information about the problem available in addition to the training data. However, in this most general form, determining a [[Model (abstract)|model]] from a finite set of samples without prior knowledge is an [[ill-posed]] problem, in the sense that a unique model may not exist. Many classifiers incorporate the general smoothness assumption that a test pattern similar to one of the training samples tends to be assigned to the same class.

In machine learning, the importance of prior knowledge can be seen from the [[No free lunch theorem]] which states that all the algorithms have the same average performance over all the problems and thus implies that to gain in performance on a certain application one must use a specialized algorithm that includes some prior knowledge about the problem.

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* [Scholkopf02], B. Scholkopf and A. Smola, "Learning with Kernels", MIT Press 2002.

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