Fast learning from sparse data

Authors:
David Maxwell Chickering;David Heckerman
Affiliations:
Microsoft Research, Redmond WA;Microsoft Research, Redmond WA
Venue:
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Year:
1999

Citing 7
Cited 4

Theory refinement on Bayesian networks

Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A Bayesian method for constructing Bayesian belief networks from databases

Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

Machine Learning
Bayesian classification (AutoClass): theory and results

Advances in knowledge discovery and data mining
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables

Machine Learning - Special issue on learning with probabilistic representations
Cached sufficient statistics for efficient machine learning with large datasets

Journal of Artificial Intelligence Research
A Bayesian approach to learning Bayesian networks with local structure

UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence

Accelerating EM for Large Databases

Machine Learning
Tractable learning of large Bayes net structures from sparse data

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using temporal data for making recommendations

UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues

The Knowledge Engineering Review

Quantified Score

Hi-index	0.00

Visualization

Abstract

We describe two techniques that significantly improve the running time of several standard machine-learning algorithms when data is sparse. The first technique is an algorithm that efficiently extracts one-way and two-way counts-either real or expected-from discrete data. Extracting such counts is a fundamental step in learning algorithms for constructing a variety of models including decision trees, decision graphs, Bayesian networks, and naive-Bayes clustering models. The second technique is an algorithm that efficiently performs the E-step of the EM algorithm (i.e., inference) when applied to a naive-Bayes clustering model. Using real-world data sets, we demonstrate a dramatic decrease in running time for algorithms that incorporate these techniques.