Inductive Transfer for Bayesian Network Structure Learning

Alexandru Niculescu-Mizil, Rich Caruana
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:339-346, 2007.

Abstract

We consider the problem of learning Bayes Net structures for related tasks. We present an algorithm for learning Bayes Net structures that takes advantage of the similarity between tasks by biasing learning toward similar structures for each task. Heuristic search is used to find a high scoring set of structures (one for each task), where the score for a set of structures is computed in a principled way. Experiments on problems generated from the ALARM and INSURANCE networks show that learning the structures for related tasks using the proposed method yields better results than learning the structures independently.

Cite this Paper

BibTeX
@InProceedings{pmlr-v2-niculescu-mizil07a, title = {Inductive Transfer for Bayesian Network Structure Learning}, author = {Niculescu-Mizil, Alexandru and Caruana, Rich}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {339--346}, year = {2007}, editor = {Meila, Marina and Shen, Xiaotong}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/niculescu-mizil07a/niculescu-mizil07a.pdf}, url = {https://proceedings.mlr.press/v2/niculescu-mizil07a.html}, abstract = {We consider the problem of learning Bayes Net structures for related tasks. We present an algorithm for learning Bayes Net structures that takes advantage of the similarity between tasks by biasing learning toward similar structures for each task. Heuristic search is used to find a high scoring set of structures (one for each task), where the score for a set of structures is computed in a principled way. Experiments on problems generated from the ALARM and INSURANCE networks show that learning the structures for related tasks using the proposed method yields better results than learning the structures independently.} }
Endnote
%0 Conference Paper %T Inductive Transfer for Bayesian Network Structure Learning %A Alexandru Niculescu-Mizil %A Rich Caruana %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-niculescu-mizil07a %I PMLR %P 339--346 %U https://proceedings.mlr.press/v2/niculescu-mizil07a.html %V 2 %X We consider the problem of learning Bayes Net structures for related tasks. We present an algorithm for learning Bayes Net structures that takes advantage of the similarity between tasks by biasing learning toward similar structures for each task. Heuristic search is used to find a high scoring set of structures (one for each task), where the score for a set of structures is computed in a principled way. Experiments on problems generated from the ALARM and INSURANCE networks show that learning the structures for related tasks using the proposed method yields better results than learning the structures independently.
RIS
TY - CPAPER TI - Inductive Transfer for Bayesian Network Structure Learning AU - Alexandru Niculescu-Mizil AU - Rich Caruana BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-niculescu-mizil07a PB - PMLR DP - Proceedings of Machine Learning Research VL - 2 SP - 339 EP - 346 L1 - http://proceedings.mlr.press/v2/niculescu-mizil07a/niculescu-mizil07a.pdf UR - https://proceedings.mlr.press/v2/niculescu-mizil07a.html AB - We consider the problem of learning Bayes Net structures for related tasks. We present an algorithm for learning Bayes Net structures that takes advantage of the similarity between tasks by biasing learning toward similar structures for each task. Heuristic search is used to find a high scoring set of structures (one for each task), where the score for a set of structures is computed in a principled way. Experiments on problems generated from the ALARM and INSURANCE networks show that learning the structures for related tasks using the proposed method yields better results than learning the structures independently. ER -
APA
Niculescu-Mizil, A. & Caruana, R.. (2007). Inductive Transfer for Bayesian Network Structure Learning. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 2:339-346 Available from https://proceedings.mlr.press/v2/niculescu-mizil07a.html.

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