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Computational learning theory

COLT (COmputational Learning Theory) is a field of machine learning.

In COLT the main interest is the computational aspects of learning, e.g., the time complexity of learning problem. The computational aspects are considered in a learning framework, like the very common one of Probably approximately correct learning.

Note that a computation is considered feasible if it can be done in polynomial time.

There are two kind of results in COLT. Possitive results - Showing the a certain class of function is learnable in polynomial time. Negative results - Showing that certain classes cannot be learned in polynomial time. Negative results were proven only by assumption. The assumptions the are common in negative results are: Computational - P<>NP Cryptographic - One way functions exits.

A list of important COLT papers


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Feature selection

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Inductive inference

Optimal O notation learning " class="external">

Negative results

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Occam's Razor

Probably approximately correct learning Error tolerance

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