Cognitively inspired classification for adapting to data distribution changes
In pattern classification, the test data is expected to lie in the domain covered by the training data. But in practical scenarios, this may not necessarily be true. To improve the adaptability, the classifier should be able to generalize well even when there are changes in the input distribution. T...
| Autors principals: | , |
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| Altres autors: | |
| Format: | Conference Paper |
| Idioma: | English |
| Publicat: |
2013
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| Matèries: | |
| Accés en línia: | https://hdl.handle.net/10356/96469 http://hdl.handle.net/10220/11982 |