Classifier Ensemble Recommendation

  • Pyry Matikainen
  • Rahul Sukthankar
  • Martial Hebert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7583)


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.


Action Recognition Weak Learner AdaBoost Algorithm Rating Store Model Recommendation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pyry Matikainen
    • 1
  • Rahul Sukthankar
    • 2
    • 1
  • Martial Hebert
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityUSA
  2. 2.Google ResearchUSA

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