Publication Type:

Conference Proceedings

Source:

Proceedings - International Conference on Pattern Recognition, Institute of Electrical and Electronics Engineers Inc., Cancun, Mexico, p.3661-3666 (2017)

URL:

http://ieeexplore.ieee.org/document/7900203/

Keywords:

agriculture, Bayes methods, Clustering algorithms, computational modeling, Data models, Prediction algorithms, Predictive models

Abstract:

<p>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.</p>

Notes:

cited By 0; Conference of 23rd International Conference on Pattern Recognition, ICPR 2016 ; Conference Date: 4 December 2016 Through 8 December 2016; Conference Code:127420

Cite this Research Publication

Dr. Shunmuga Velayutham C., S, S., S, R., S, G., Dr. Bhagavathi Sivakumar P., and S, V., “Bayesian nonparametric Multiple Instance Regression”, Proceedings - International Conference on Pattern Recognition. Institute of Electrical and Electronics Engineers Inc., Cancun, Mexico, pp. 3661-3666, 2017.

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