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http://hdl.handle.net/2152/345
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| Title: | Adaptive hierarchical classification with limited training data [electronic resource] |
| Authors: | Morgan, Joseph Troy.
Crawford, Melba M. |
| Keywords: | Operations Research.
Agriculture, Range Management. |
| Issue Date: | 27-Jan-2006 |
| Publisher: | The University of Texas at Austin |
| Abstract: | This research focused on the development of a hierarchical approach for classification that is robust with respect to training data that is limited both in quantity and spatial extent. Many difficult classification problems involve a high dimensional input and output space (candidate labels). Due to the curse of dimensionality, it is necessary to reduce the size of the input space when there is only a limited quantity of training data available. While a significant amount of research has focused on transforming the input space into a reduced feature space that accurately discriminates between the classes in a fixed output space, traditional approaches fail to capitalize on the domain knowledge and flexibility gained by transforming the feature space and the output space simultaneously. A new approach is proposed that utilizes domain knowledge, which is automatically discovered from the data, to combat the small sample size problem. |
| URI: | http://hdl.handle.net/2152/345 |
| Appears in Collections: | Theses and Dissertations from The University of Texas at Austin
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