December 2018
Intermediate to advanced
318 pages
8h 28m
English
Content preview from Hands-On Machine Learning for Cybersecurity
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Bayesian parameter averaging
This is a type of ensemble learning where the Bayesian parameter average model approximates the optimal classifier by taking hypotheses from hypothesis spaces and then applying Bayes' algorithm to them. Here the hypothesis spaces are sampled by using algorithms like Monte Carlo sampling, Gibbs sampling, and so on. These are also known as Bayesian model averaging.
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