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Carnegie Mellon Robotics Institute

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Abstract

The problem of training classiers from limited data is one that particularly affects large-scale and social applications, and as a result, although carefully trained machine learning forms the backbone of many current techniques in research, it sees dramatically fewer applications for end-users. Recently we demonstrated a technique for selecting or recommending a single good classier from a large library even with highly impoverished training data. We consider alternatives for extending our recommendation technique to sets of classiers, including a modification to the AdaBoost algorithm that incorporates recommendation. Evaluating on an action recognition problem, we present two viable methods for extending model recommendation to sets.

Keywords

machine learning, classification, action recognition, multi-task learning, collaborative filtering, recommendation

Notes

Sponsor: This work was partially funded by the Army Research Laboratory under Cooperative Agreement #W911NF-10-2-0061.

Text Reference

Pyry Matikainen, Rahul Sukthankar, and Martial Hebert, "Classier Ensemble Recommendation," Workshop on Web-scale Vision and Social Media, ECCV 2012, August, 2012.

BibTeX Reference

@inproceedings{Matikainen_2012_7260,
   author = "Pyry Matikainen and Rahul Sukthankar and Martial Hebert",
   title = "Classier Ensemble Recommendation",
   booktitle = "Workshop on Web-scale Vision and Social Media, ECCV 2012",
   month = "August",
   year = "2012",
}

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