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Concept learning via granular computing: A cognitive viewpoint
Concepts are the most fundamental units of cognition in philosophy and how to learn concepts from various aspects in the real world is the main concern within the domain of conceptual knowledge presentation and processing. In order to improve efficiency and flexibility of concept learning, in this p...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7094283/ https://www.ncbi.nlm.nih.gov/pubmed/32226109 http://dx.doi.org/10.1016/j.ins.2014.12.010 |
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author | Li, Jinhai Mei, Changlin Xu, Weihua Qian, Yuhua |
author_facet | Li, Jinhai Mei, Changlin Xu, Weihua Qian, Yuhua |
author_sort | Li, Jinhai |
collection | PubMed |
description | Concepts are the most fundamental units of cognition in philosophy and how to learn concepts from various aspects in the real world is the main concern within the domain of conceptual knowledge presentation and processing. In order to improve efficiency and flexibility of concept learning, in this paper we discuss concept learning via granular computing from the point of view of cognitive computing. More precisely, cognitive mechanism of forming concepts is analyzed based on the principles from philosophy and cognitive psychology, including how to model concept-forming cognitive operators, define cognitive concepts and establish cognitive concept structure. Granular computing is then combined with the cognitive concept structure to improve efficiency of concept learning. Furthermore, we put forward a cognitive computing system which is the initial environment to learn composite concepts and can integrate past experiences into itself for enhancing flexibility of concept learning. Also, we investigate cognitive processes whose aims are to deal with the problem of learning one exact or two approximate cognitive concepts from a given object set, attribute set or pair of object and attribute sets. |
format | Online Article Text |
id | pubmed-7094283 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Elsevier Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-70942832020-03-25 Concept learning via granular computing: A cognitive viewpoint Li, Jinhai Mei, Changlin Xu, Weihua Qian, Yuhua Inf Sci (N Y) Article Concepts are the most fundamental units of cognition in philosophy and how to learn concepts from various aspects in the real world is the main concern within the domain of conceptual knowledge presentation and processing. In order to improve efficiency and flexibility of concept learning, in this paper we discuss concept learning via granular computing from the point of view of cognitive computing. More precisely, cognitive mechanism of forming concepts is analyzed based on the principles from philosophy and cognitive psychology, including how to model concept-forming cognitive operators, define cognitive concepts and establish cognitive concept structure. Granular computing is then combined with the cognitive concept structure to improve efficiency of concept learning. Furthermore, we put forward a cognitive computing system which is the initial environment to learn composite concepts and can integrate past experiences into itself for enhancing flexibility of concept learning. Also, we investigate cognitive processes whose aims are to deal with the problem of learning one exact or two approximate cognitive concepts from a given object set, attribute set or pair of object and attribute sets. Elsevier Inc. 2015-03-20 2014-12-12 /pmc/articles/PMC7094283/ /pubmed/32226109 http://dx.doi.org/10.1016/j.ins.2014.12.010 Text en Copyright © 2014 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Li, Jinhai Mei, Changlin Xu, Weihua Qian, Yuhua Concept learning via granular computing: A cognitive viewpoint |
title | Concept learning via granular computing: A cognitive viewpoint |
title_full | Concept learning via granular computing: A cognitive viewpoint |
title_fullStr | Concept learning via granular computing: A cognitive viewpoint |
title_full_unstemmed | Concept learning via granular computing: A cognitive viewpoint |
title_short | Concept learning via granular computing: A cognitive viewpoint |
title_sort | concept learning via granular computing: a cognitive viewpoint |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7094283/ https://www.ncbi.nlm.nih.gov/pubmed/32226109 http://dx.doi.org/10.1016/j.ins.2014.12.010 |
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