research-article

Discriminative parameter learning for Bayesian networks

Authors:

Jiang Su,

Harry Zhang,

Charles X. Ling,

Stan MatwinAuthors Info & Claims

ICML '08: Proceedings of the 25th international conference on Machine learning

Pages 1016 - 1023

https://doi.org/10.1145/1390156.1390284

Published: 05 July 2008 Publication History

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Abstract

Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: generative and discriminative learning. While generative parameter learning is more efficient, discriminative parameter learning is more effective. In this paper, we propose a simple, efficient, and effective discriminative parameter learning method, called Discriminative Frequency Estimate (DFE), which learns parameters by discriminatively computing frequencies from data. Empirical studies show that the DFE algorithm integrates the advantages of both generative and discriminative learning: it performs as well as the state-of-the-art discriminative parameter learning method ELR in accuracy, but is significantly more efficient.

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Mei ZLi YGao XWang WFeng QRu X(2023)Bayesian network parameter learning with constraint balance2023 International Conference on Cyber-Physical Social Intelligence (ICCSI)10.1109/ICCSI58851.2023.10304017(209-214)Online publication date: 20-Oct-2023
https://doi.org/10.1109/ICCSI58851.2023.10304017
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https://dl.acm.org/doi/10.1016/j.ipm.2023.103381
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Published In

ICML '08: Proceedings of the 25th international conference on Machine learning

July 2008

1310 pages

ISBN:9781605582054

DOI:10.1145/1390156

General Chair:
William Cohen
Carnegie Mellon University
,
Program Chairs:
Andrew McCallum
University of Massachusetts Amherst
,
Sam Roweis
University of Toronto and Google

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Association for Computing Machinery

New York, NY, United States

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Published: 05 July 2008

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ICML '08: The 25th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

July 5 - 9, 2008

Helsinki, Finland

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https://doi.org/10.1109/ICCSI58851.2023.10304017
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Discriminative versus generative parameter and structure learning of Bayesian network classifiers

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Discriminative versus generative parameter and structure learning of Bayesian network classifiers

Combining deep generative and discriminative models for Bayesian semi-supervised learning

Multiple instance learning by discriminative training of Markov networks

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