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Classi er Ensemble Recommendation

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

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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.

BibTeX Reference
title = {Classi er Ensemble Recommendation},
author = {Pyry K. Matikainen and Rahul Sukthankar and Martial Hebert},
booktitle = {Workshop on Web-scale Vision and Social Media, ECCV 2012},
keyword = {machine learning, classification, action recognition, multi-task learning, collaborative filtering, recommendation},
sponsor = {This work was partially funded by the Army Research Laboratory under Cooperative Agreement #W911NF-10-2-0061.},
school = {Robotics Institute , Carnegie Mellon University},
month = {August},
year = {2012},
address = {Pittsburgh, PA},