Classifier Ensemble Recommendation
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.
Cite
Text
Matikainen et al. "Classifier Ensemble Recommendation." European Conference on Computer Vision Workshops, 2012. doi:10.1007/978-3-642-33863-2_21Markdown
[Matikainen et al. "Classifier Ensemble Recommendation." European Conference on Computer Vision Workshops, 2012.](https://mlanthology.org/eccvw/2012/matikainen2012eccvw-classifier/) doi:10.1007/978-3-642-33863-2_21BibTeX
@inproceedings{matikainen2012eccvw-classifier,
title = {{Classifier Ensemble Recommendation}},
author = {Matikainen, Pyry and Sukthankar, Rahul and Hebert, Martial},
booktitle = {European Conference on Computer Vision Workshops},
year = {2012},
pages = {209-218},
doi = {10.1007/978-3-642-33863-2_21},
url = {https://mlanthology.org/eccvw/2012/matikainen2012eccvw-classifier/}
}