Kok, J.K. (1998) Novelty detection for neural pattern classification. Master's Thesis / Essay, Computing Science.
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Abstract
Complex forms of pattern recognition is more widely used these days. Complex recognition problems are characterized pattern classes that are hard to separate and high demands on recognition speeds. This is why neural pattern classifiers have become more important. Especially the multi—layer perceptron (MLP) is very suitable for complex classification, since this method combines high classification speeds with high accuracy. In a safety—critical environment like in medical and industrial applications pattern classifiers, like any other system, must meet high robustness standards. In pattern recognition one aspect of robustness is the reaction of a classifier when a novelty is presented to it. A novelty is an input pattern which differs significantly from the patterns used to develop the classifier. For most statistical and neural pattern classifiers is a novelty at the input easy to detect. However, an MLP based classifier is not capable to recognize an input as novelty and will simply classify the pattern in one of the known classes and the result will be invalid. To guarantee the reliability of the classifier an extension with a novelty detection method is needed. The extension must work in such a way that the strenghts of the MLP classifier remain unaffected. To avoid interference with the classification accuracy of the MLP is searched for a separate novelty detection system that can work in parallel with the MLP. A system that investigates the input pattern and determines the degree of novelty. Existing forms of these dedicated novelty detectors are either too slow to be used for complex systems or the optimal setting of their parameters are hard to determine. To solve these problems a new novelty detection method is developed. Using this method fast novelty detection systems can be build. The appropriate parameter settings can easily be found using measurable quantities that reflect the quality of the system.
| Item Type: | Thesis (Master's Thesis / Essay) |
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| Degree programme: | Computing Science |
| Thesis type: | Master's Thesis / Essay |
| Language: | English |
| Date Deposited: | 15 Feb 2018 07:29 |
| Last Modified: | 15 Feb 2018 07:29 |
| URI: | https://fse.studenttheses.ub.rug.nl/id/eprint/8813 |
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