Hybrid soft categorisation in conceptual spaces
Conference Publication ResearchOnline@JCUAbstract
Understanding the process of categorization is of great importance for building intelligent agents. Formulated categories help agents find information easier and understand the external world better. Instance-based categorization and prototype-based categorization have been two dominant approaches in the AI community. However, they share some drawbacks in common. First, they are crisp boundary based hard categorizations (similar to classification). Second, they are not well-suited for dynamic category learning and formation. In this paper, we propose a hybrid soft categorization in the conceptual level that overcomes these drawbacks. The hybrid soft categorization merges the two popular hard categorizations and provides a robust fuzzy boundary-based soft categorization.
Journal
N/A
Publication Name
Fourth International Conference on Hybrid Intelligent Systems
Volume
N/A
ISBN/ISSN
978-0-7695-2291-3
Edition
N/A
Issue
N/A
Pages Count
6
Location
Kitakyushu, Japan
Publisher
IEEE Computer Society
Publisher Url
N/A
Publisher Location
Los Alamitos, Calif.
Publish Date
N/A
Url
N/A
Date
N/A
EISSN
N/A
DOI
10.1109/ICHIS.2004.57