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...

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Main Authors:	Sit, Wing Yee, Mao, K. Z.
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	DRNTU::Engineering::Electrical and electronic engineering
Online Access:	https://hdl.handle.net/10356/96469 http://hdl.handle.net/10220/11982
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.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.
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering Sit, Wing Yee Mao, K. Z. Cognitively inspired classification for adapting to data distribution changes
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.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Sit, Wing Yee Mao, K. Z.
format	Conference or Workshop Item
author	Sit, Wing Yee Mao, K. Z.
author_sort	Sit, Wing Yee
title	Cognitively inspired classification for adapting to data distribution changes
title_short	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_sort	cognitively inspired classification for adapting to data distribution changes
publishDate	2013
url	https://hdl.handle.net/10356/96469 http://hdl.handle.net/10220/11982
_version_	1681047019178688512

Cognitively inspired classification for adapting to data distribution changes

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