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...
| Autori principali: | , |
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| Altri autori: | |
| Natura: | Conference Paper |
| Lingua: | English |
| Pubblicazione: |
2013
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| Accesso online: | https://hdl.handle.net/10356/96469 http://hdl.handle.net/10220/11982 |
| _version_ | 1826129665438253056 |
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| author | Sit, Wing Yee Mao, K. Z. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Sit, Wing Yee Mao, K. Z. |
| author_sort | Sit, Wing Yee |
| collection | NTU |
| description | 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. This paper proposes a cognitively inspired classification framework based on rules and exemplars. It can generalize well even for samples falling outside the region covered by the training data. |
| first_indexed | 2024-10-01T07:44:14Z |
| format | Conference Paper |
| id | ntu-10356/96469 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:44:14Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/964692020-03-07T13:24:47Z Cognitively inspired classification for adapting to data distribution changes Sit, Wing Yee Mao, K. Z. School of Electrical and Electronic Engineering IEEE Conference on Evolving and Adaptive Intelligent Systems (2012 : Madrid, Spain) DRNTU::Engineering::Electrical and electronic engineering 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. This paper proposes a cognitively inspired classification framework based on rules and exemplars. It can generalize well even for samples falling outside the region covered by the training data. 2013-07-22T06:16:46Z 2019-12-06T19:31:11Z 2013-07-22T06:16:46Z 2019-12-06T19:31:11Z 2012 2012 Conference Paper Sit, W. Y., & Mao, K. Z. (2012). Cognitively inspired classification for adapting to data distribution changes. 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). https://hdl.handle.net/10356/96469 http://hdl.handle.net/10220/11982 10.1109/EAIS.2012.6232802 en © 2012 IEEE. |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Sit, Wing Yee Mao, K. Z. Cognitively inspired classification for adapting to data distribution changes |
| title | Cognitively inspired classification for adapting to data distribution changes |
| title_full | Cognitively inspired classification for adapting to data distribution changes |
| title_fullStr | Cognitively inspired classification for adapting to data distribution changes |
| title_full_unstemmed | Cognitively inspired classification for adapting to data distribution changes |
| title_short | Cognitively inspired classification for adapting to data distribution changes |
| title_sort | cognitively inspired classification for adapting to data distribution changes |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/96469 http://hdl.handle.net/10220/11982 |
| work_keys_str_mv | AT sitwingyee cognitivelyinspiredclassificationforadaptingtodatadistributionchanges AT maokz cognitivelyinspiredclassificationforadaptingtodatadistributionchanges |