Journal article
A distributed learning algorithm for Bayesian inference networks
IEEE transactions on knowledge and data engineering, Vol.14(1), pp.93-105
01/2002
DOI: 10.1109/69.979975
Abstract
We present a new distributed algorithm for computing the minimum description length (MDL) in learning Bayesian inference networks from data. Our learning algorithm exploits both properties of the MDL-based score metric and a distributed, asynchronous, adaptive search technique called nagging. Nagging is intrinsically fault-tolerant, has dynamic load balancing features, and scales well. We demonstrate the viability, effectiveness, and scalability of our approach empirically with several experiments using networked machines. More specifically, we show that our distributed algorithm can provide optimal solutions for larger problems as well as good solutions for Bayesian networks of up to 150 variables.
Details
- Title: Subtitle
- A distributed learning algorithm for Bayesian inference networks
- Creators
- Wai Lam - Chinese University of Hong KongA.M Segre - [Department of Management Seciences, University of Lowa, Iowa, IA, USA]
- Resource Type
- Journal article
- Publication Details
- IEEE transactions on knowledge and data engineering, Vol.14(1), pp.93-105
- Publisher
- IEEE
- DOI
- 10.1109/69.979975
- ISSN
- 1041-4347
- eISSN
- 1558-2191
- Language
- English
- Date published
- 01/2002
- Academic Unit
- Nursing; Fraternal Order of Eagles Diabetes Research Center; Computer Science
- Record Identifier
- 9984259477602771
Metrics
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