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Classifier Ensemble Recommendation
Pyry Matikainen1, Rahul Sukthankar2, 1, and Martial Hebert1
1The Robotics Institute, Carnegie Mellon University, USA
pmatikai@cs.cmu.edu
rahuls@cs.cmu.edu
hebert@ri.cmu.edu
2Google Research, USA
Abstract. The problem of training classifiers 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 classifier from a large library even with highly impoverished training data. We consider alternatives for extending our recommendation technique to sets of classifiers, 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.
LNCS 7583, p. 209 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012