Deakin University
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Towards scalable Bayesian nonparametric methods for data analytics

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thesis
posted on 2017-01-01, 00:00 authored by V Huynh
Resorting big data to actionable information involves dealing with four dimensions of challenges in big data (called four V’s): volume, variety, velocity, veracity. In this study, we seek for novel Bayesian nonparametric models and scalable learning algorithms which can deal with these challenges of the big data era.

History

Pagination

xix, 168, 4 pages : illustrations, tables, graphs, some coloured

Open access

  • Yes

Material type

thesis

Resource type

thesis

Language

eng

Degree type

Research doctorate

Degree name

PhD

Copyright notice

The author.

Editor/Contributor(s)

S Venkatesh, D Phung

Faculty

Faculty of Science

School

Engineering and Built Environment