Relationship between classifier performance and distributional complexity for small samples /
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Thesis eBook |
| Language: | English |
| Published: |
[College Station, Tex.] :
[Texas A & M University],
[2003]
|
| Subjects: | |
| Online Access: | Link to OAK Trust copy |
| Abstract: | 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. |
|---|---|
| Item Description: | Vita. Abstract. "Major Subject: Electrical Engineering" Title from author supplied metadata (automated record created on Oct. 15, 2004.) Electronic resource. |
| Physical Description: | 1 online resource. |
| Format: | System requirements: Adobe Acrobat Reader. |
| Bibliography: | Includes bibliographical references. |