Pattern recognition problems involve two main issues: feature formulation and classifier design. This thesis is concerned with the latter. Numerous algorithms for the design of pattern recognition systems have been published, and the algorithm detailed herein is a new approach--specific to the design of decision tree classifiers. It involves a top-down strategy, optimizing the root node decision and then subsequently its children. To assess various pattern space partitions, the Tie statistical distance measure quantified the separability of potential cluster groupings. Additionally, a separate neural network was employed at each of the tree decision nodes. Results from the application of this methodology to the regional labeling of panchromatic images suggest it is a suitable approach.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/277881 |
| Date | January 1991 |
| Creators | Rosten, David Paul, 1967- |
| Contributors | Hunt, Bobby R. |
| Publisher | The University of Arizona. |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | text, Thesis-Reproduction (electronic) |
| Rights | Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author. |
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