ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
The Journal of Machine Learning Research
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Face Verification Using GaborWavelets and AdaBoost
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
HMM-Based Acoustic Event Detection with AdaBoost Feature Selection
Multimodal Technologies for Perception of Humans
Kernel-Based Inductive Transfer
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Multi-task learning for boosting with application to web search ranking
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Boosting Multi-Task Weak Learners with Applications to Textual and Social Data
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Model recommendation for action recognition
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Action bank: A high-level representation of activity in video
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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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.