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ML
2010
ACM

Inductive transfer for learning Bayesian networks

13 years 2 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with different conditions; or in industrial diagnosis, where there is equipment with similar specifications. In these cases it is common to have plenty of data for some scenarios but very little for others. In order to learn accurate models for rare cases, it is desirable to use data and knowledge from similar cases; a technique known as transfer learning. In this paper we propose an inductive transfer learning method for Bayesian networks, that considers both structure and parameter learning. For structure learning we use conditional independence tests, by combining measures from the target task with those obtained from one or more auxiliary tasks, using a novel weighted sum of the conditional independence measures. For parameter learning, we propose two variants of the linear pool for probability aggregation, ...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
Added 29 Jan 2011
Updated 29 Jan 2011
Type Journal
Year 2010
Where ML
Authors Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
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