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. Attached Files Name Description MIMEType Size Downloads Title Bayesian nonparametric Multiple Instance Regression Author(s) Subramanian, Saravanan orcid.org/0000-0003-2247-850X Rana, Santu orcid.org/0000-0002-3308-1930 Gupta, Sunil orcid.org/0000-0001-8675-6631 Sivakumar, P. Bagavathi Velayutham, C. Shunmuga 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 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics 2018 ERA Submission Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 01 May 2017, 13:11:59 EST Tue, 02 May 2017, 05:00:29 EST Tue, 02 May 2017, 08:20:26 EST Sat, 27 May 2017, 02:32:18 EST Mon, 29 May 2017, 13:15:55 EST Tue, 30 May 2017, 06:06:49 EST Fri, 02 Jun 2017, 08:37:29 EST Fri, 02 Jun 2017, 08:41:53 EST Fri, 02 Jun 2017, 08:44:26 EST Sat, 07 Oct 2017, 01:30:38 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 139 Abstract Views, 10 File Downloads  -  Detailed Statistics Created: Mon, 29 May 2017, 13:15:55 EST

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