Given a limited number of samples for classification, several issues arise with respect to design, performance and analysis of classifiers. This is especially so in the case of microarray-based classification. In this paper, we use a complexity measure based mixture model to study classifier performance for small sample problems. The motivation behind such a study is to determine the conditions under which a certain class of classifiers is suitable for classification, subject to the constraint of a limited number of samples being available. Classifier study in terms of the VC dimension of a learning machine is also discussed.
| Identifer | oai:union.ndltd.org:TEXASAandM/oai:repository.tamu.edu:1969.1/1201 |
| Date | 15 November 2004 |
| Creators | Attoor, Sanju Nair |
| Contributors | Dougherty, Edward R., Serpedin, Erchin, Liu, Jyh-Charn, Narayanan, Krishna |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Electronic Thesis, text |
| Format | 276697 bytes, 53293 bytes, electronic, application/pdf, text/plain, born digital |
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