Radial basis function network for multi-task learning


Journal Article

We extend radial basis function (RBF) networks to the scenario in which multiple correlated tasks are learned simultaneously, and present the corresponding learning algorithms. We develop the algorithms for learning the network structure, in either a supervised or unsupervised manner. Training data may also be actively selected to improve the network's generalization to test data. Experimental results based on real data demonstrate the advantage of the proposed algorithms and support our conclusions.

Duke Authors

Cited Authors

  • Liao, X; Carin, L

Published Date

  • December 1, 2005

Published In

Start / End Page

  • 795 - 802

International Standard Serial Number (ISSN)

  • 1049-5258

Citation Source

  • Scopus