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DECISION THEORY CLASSIFICATION OF HIGH-DIMENSIONAL VECTORS BASED ON SMALL SAMPLES
| Title: | DECISION THEORY CLASSIFICATION OF HIGH-DIMENSIONAL VECTORS BASED ON SMALL SAMPLES. |
 41 views 19 downloads |
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| Name(s): |
Bradshaw, David, Author Pensky, Marianna, Committee Chair University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2005 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| Abstract/Description: | In this paper, we review existing classification techniques and suggest an entirely new procedure for the classification of high-dimensional vectors on the basis of a few training samples. The proposed method is based on the Bayesian paradigm and provides posterior probabilities that a new vector belongs to each of the classes, therefore it adapts naturally to any number of classes. Our classification technique is based on a small vector which is related to the projection of the observation onto the space spanned by the training samples. This is achieved by employing matrix-variate distributions in classification, which is an entirely new idea. In addition, our method mimics time-tested classification techniques based on the assumption of normally distributed samples. By assuming that the samples have a matrix-variate normal distribution, we are able to replace classification on the basis of a large covariance matrix with classification on the basis of a smaller matrix that describes the relationship of sample vectors to each other. | |
| Identifier: | CFE0000753 (IID), ucf:46593 (fedora) | |
| Note(s): |
2005-12-01 Ph.D. Arts and Sciences, Department of Mathematics Doctorate This record was generated from author submitted information. |
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| Subject(s): |
Support Vector Machine decision theory posterior probabilities matrix-variate normal |
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| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0000753 | |
| Restrictions on Access: | public | |
| Host Institution: | UCF |
