Classi er Ensemble Recommendation

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


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Abstract
The problem of training classi ers 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 classi er from a large library even with highly impoverished training data. We consider alternatives for extending our recommendation technique to sets of classi ers, 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 K. Matikainen, Rahul Sukthankar, and Martial Hebert, "Classi er Ensemble Recommendation," Workshop on Web-scale Vision and Social Media, ECCV 2012, August, 2012.

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