Decision Region Connectivity Analysis: A method for analyzing high-dimensional classifiers (2001)
| Venue: | Machine Learning |
| Citations: | 2 - 0 self |
BibTeX
@INPROCEEDINGS{Melnik01decisionregion,
author = {Ofer Melnik},
title = {Decision Region Connectivity Analysis: A method for analyzing high-dimensional classifiers},
booktitle = {Machine Learning},
year = {2001}
}
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Abstract
In this paper we present a method to extract qualitative information from any classification model that uses decision regions to generalize (e.g., feedforward neural nets, SVMs, etc). The method's complexity is independent of the dimensionality of the input data or model, making it computationally feasible for the analysis of even very high-dimensional models. The qualitative information extracted by the method can be directly used to analyze the classification strategies employed by a model, and also to compare strategies across diferent model types.
