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.
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.
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