Book chapter
Learning Pseudo-independent Models: Analytical and Experimental Results
Advances in Artificial Intelligence
19 May 2000
Abstract
Most algorithms to learn belief networks use single-link looka-head search to be efficient. It has been shown that such search procedures are problematic when applied to learning pseudo-independent (PI) models. Furthermore, some researchers have questioned whether PI models exist in practice.
We present two non-trivial PI models which derive from a social study dataset. For one of them, the learned PI model reached ultimate prediction accuracy achievable given the data only, while using slightly more inference time than the learned non-PI model. These models provide evidence that PI models are not simply mathematical constructs.
To develop efficient algorithms to learn PI models effectively we benefit from studying and understanding such models in depth. We further analyze how multiple PI submodels may interact in a larger domain model. Using this result, we show that the RML algorithm for learning PI models can learn more complex PI models than previously known.
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Details
- Title
- Learning Pseudo-independent Models: Analytical and Experimental Results
- Creators
- Yang Xiang - University of Massachusetts SystemXiaohua Hu - Fulcrum Technologies Inc.Nicholas J. CerconeHoward J. Hamilton - University of Regina
- Publication Details
- Advances in Artificial Intelligence
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000166853200019
- Scopus ID
- 2-s2.0-4344682850
- Other Identifier
- 991019189019704721
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence