Computational Learning Theory

Intermediate

A theoretical framework analyzing what classes of functions can be learned, how efficiently, and with what guarantees.

Full Definition

A theoretical framework analyzing what classes of functions can be learned, how efficiently, and with what guarantees.

Keywords

PAC generalization bounds

Domains

AI Economics & Strategy

Related Terms

PAC Learning related to

A model is PAC-learnable if it can, with high probability, learn an approximately correct hypothesis from finite samp...

 VC Dimension related to

A measure of a model class’s expressive capacity based on its ability to shatter datasets.

 Rademacher Complexity related to

Measures a model’s ability to fit random noise; used to bound generalization error.

 Bias Term related to

Systematic error introduced by simplifying assumptions in a learning algorithm.

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