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RI | Publications | Classification in Very High Dimensional Problems with Handfuls of Examples
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Text only version of this site
Classification in Very High Dimensional Problems with Handfuls of Examples
M. Palatucci and T. Mitchell
Principles and Practice of
Knowledge Discovery in Databases (ECML/PKDD), Springer-Verlag, September, 2007.
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| Abstract |
Modern classification techniques perform well when the number of training examples exceed the number of features. If, however, the number of features greatly exceed the number of training examples, then these same techniques can fail. To address this problem, we present a hierarchical Bayesian framework that shares information between features by modeling similarities between their parameters. We believe this approach is applicable to many sparse, high dimensional problems and especially relevant to those with both spatial and temporal components. One such problem is fMRI time series, and we present a case study that shows how we can successfully classify in this domain with 80,000 original features and only 2 training examples per class.
| Notes |
Number of pages: 12
| Text Reference |
M. Palatucci and T. Mitchell, "Classification in Very High Dimensional Problems with Handfuls of Examples," Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), Springer-Verlag, September, 2007.
| BibTeX Reference |
@inproceedings{Palatucci_2007_5786,
author = "Mark Palatucci and Tom Mitchell",
title = "Classification in Very High Dimensional Problems with Handfuls of Examples",
booktitle = "Principles and Practice of
Knowledge Discovery in Databases (ECML/PKDD)",
month = "September",
year = "2007",
publisher = "Springer-Verlag"
}