Construct · 1 fields · 1 citations

PAC-Bayes bound: a standalone construct

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Computational learning theory

a generalization guarantee for a posterior over hypotheses that depends on its divergence from a prior chosen before seeing the data.

In computational learning theory, PAC-Bayes bound refers to a generalization guarantee for a posterior over hypotheses that depends on its divergence from a prior chosen before seeing the data. This meaning comes from McAllester (1999) in Machine Learning.[1]

Unit
Recovery means
Measured by

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Nearby constructs

divergence4 fields · 4 citationsjingle · one word, four constructs generalization6 fields · 16 citationsjingle · one name, six constructs learning6 fields · 14 citationsjingle · one name, six constructs

References

McAllester, D. A. (1999). Some PAC-Bayesian Theorems. Machine Learning, 37(3), 355-363. https://doi.org/10.1023/a:1007618624809

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