Prior knowledge for pattern recognition: Difference between revisions

Revision as of 12:32, 16 January 2012

Revision as of 13:56, 16 January 2012

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== Definition ==

Prior knowledge<ref>B. Scholkopf and A. Smola, "Learning with Kernels", MIT Press 2002.</ref> 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.

The importance of prior knowledge in machine learning is suggested by its role in search and optimization. Loosely, the [[No free lunch in search and optimization|no free lunch theorem]] states that all search algorithms have the same average performance over all 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. <!-- This sentence is still not right. Read the "no free lunch" article to see why.

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The different types of prior knowledge encountered in pattern recognition are now regrouped under two main categories: class-invariance and knowledge on the data.

proir knowledge is a mental structure that describes our knowledge and experiences gained during the course of our life and how old experiences are used to understand new ones.

== Class-invariance ==