Abstract
In the context of statistical machine learning, sparse learning is a procedure that seeks a reconciliation between two competing aspects of a statistical model: good predictive power and interpretability. In a Bayesian setting, sparse learning methods invoke sparsity inducing priors to explicitly encode this tradeoff in a principled manner.
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
Date of Award
January 2016
Recommended Citation
Zilqurnain Naqvi, Syed Abbas Zilqurnain Naqvi, "Efficient Sparse Bayesian Learning using Spike-and-Slab Priors" (2016). Open Access Dissertations. 1402.
https://docs.lib.purdue.edu/open_access_dissertations/1402
First Advisor
Yuan Qi
Committee Member 1
Charles A Bouman
Committee Member 2
Jennifer Neville
Committee Member 3
David F Gleich
Committee Member 4
Ninghui Li
COinS