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Multi-task learning for underwater object classification

Publication ,  Journal Article
Stack, JR; Crosby, F; McDonald, RJ; Xue, Y; Carin, L
Published in: Proceedings of SPIE - The International Society for Optical Engineering
November 15, 2007

The purpose of this research is to jointly learn multiple classification tasks by appropriately sharing information between similar tasks. In this setting, examples of different tasks include the discrimination of targets from non-targets by different sonars or by the same sonar operating in sufficiently different environments. This is known as multi-task learning (MTL) and is accomplished via a Bayesian approach whereby the learned parameters for classifiers of similar tasks are drawn from a common prior. To learn which tasks are similar and the appropriate priors a Dirichlet process is employed and solved using mean field variational Bayesian inference. The result is that for many real-world instances where training data is limited MTL exhibits a significant improvement over both learning individual classifiers for each task as well as pooling all data and training one overall classifier. The performance of this method is demonstrated on simulated data and experimental data from multiple imaging sonars operating over multiple environments.

Duke Scholars

Published In

Proceedings of SPIE - The International Society for Optical Engineering

DOI

ISSN

0277-786X

Publication Date

November 15, 2007

Volume

6553

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4009 Electronics, sensors and digital hardware
  • 4006 Communications engineering
 

Citation

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Stack, J. R., Crosby, F., McDonald, R. J., Xue, Y., & Carin, L. (2007). Multi-task learning for underwater object classification. Proceedings of SPIE - The International Society for Optical Engineering, 6553. https://doi.org/10.1117/12.717071
Stack, J. R., F. Crosby, R. J. McDonald, Y. Xue, and L. Carin. “Multi-task learning for underwater object classification.” Proceedings of SPIE - The International Society for Optical Engineering 6553 (November 15, 2007). https://doi.org/10.1117/12.717071.
Stack JR, Crosby F, McDonald RJ, Xue Y, Carin L. Multi-task learning for underwater object classification. Proceedings of SPIE - The International Society for Optical Engineering. 2007 Nov 15;6553.
Stack, J. R., et al. “Multi-task learning for underwater object classification.” Proceedings of SPIE - The International Society for Optical Engineering, vol. 6553, Nov. 2007. Scopus, doi:10.1117/12.717071.
Stack JR, Crosby F, McDonald RJ, Xue Y, Carin L. Multi-task learning for underwater object classification. Proceedings of SPIE - The International Society for Optical Engineering. 2007 Nov 15;6553.

Published In

Proceedings of SPIE - The International Society for Optical Engineering

DOI

ISSN

0277-786X

Publication Date

November 15, 2007

Volume

6553

Related Subject Headings

  • 5102 Atomic, molecular and optical physics
  • 4009 Electronics, sensors and digital hardware
  • 4006 Communications engineering