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Bayesian nonparametric Multiple Instance Regression

Subramanian, Saravanan, Rana, Santu, Gupta, Sunil, Sivakumar, P. Bagavathi, Velayutham, C. Shunmuga and Venkatesh, Svetha 2016, Bayesian nonparametric Multiple Instance Regression, in ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition, IEEE, Piscataway, N.J., pp. 3661-3666, doi: 10.1109/ICPR.2016.7900203.

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Title Bayesian nonparametric Multiple Instance Regression
Author(s) Subramanian, SaravananORCID iD for Subramanian, Saravanan orcid.org/0000-0003-2247-850X
Rana, SantuORCID iD for Rana, Santu orcid.org/0000-0002-3308-1930
Gupta, SunilORCID iD for Gupta, Sunil orcid.org/0000-0001-8675-6631
Sivakumar, P. Bagavathi
Velayutham, C. Shunmuga
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name Pattern Recognition. International Conference (23rd : 2016 : Cancun, Mexico)
Conference location Cancun, Mexico
Conference dates 4-8 Dec. 2016
Title of proceedings ICPR 2016: Proceedings of the 23rd International Conference on Pattern Recognition
Publication date 2016
Conference series Pattern Recognition International Conference
Start page 3661
End page 3666
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Multiple Instance Regression jointly models a set of instances and its corresponding real-valued output. We present a novel multiple instance regression model that infers a subset of instances in each bag that best describes the bag label and uses them to learn a predictive model in a unified framework. We assume that instances in each bag are drawn from a mixture distribution and thus naturally form groups, and instances from one of this group explain the bag label. The largest cluster is assumed to be correlated with the label. We evaluate this model on the crop yield prediction and aerosol depth prediction problems. The predictive accuracy of our model is better than the state of the art MIR methods.
ISBN 9781509048472
Language eng
DOI 10.1109/ICPR.2016.7900203
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 0 Not Applicable
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30094578

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