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Machine Learning
book

Machine Learning

by Sergios Theodoridis
April 2015
Intermediate to advanced content levelIntermediate to advanced
1062 pages
40h 35m
English
Academic Press
Content preview from Machine Learning

13.6 Sparse Bayesian Learning (SBL)

In Section 13.3, the prior for each one of the unknown parameters, θk,k = 0,1,…,K − 1, were given the liberty to have their own variances, σk2:=1αksi132_e. In turn, these variances were treated as hidden random variables and a prior was assigned to each of them in terms of a number of hyperparameters.

In [75, 81], the model was slightly modified. The concept of using different variances for the priors was retained, but the variances were treated as deterministic parameters and not as random ones.2 In this context, the task becomes a generalization of the one treated in Section 12.6, and it is built upon the following ...

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Publisher Resources

ISBN: 9780128015223