| Download | - View accepted manuscript: Case learning for CBR-based collision avoidance systems (PDF, 965 KiB)
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| DOI | Resolve DOI: https://doi.org/10.1007/s10489-010-0262-z |
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| Author | Search for: Liu, Yuhong; Search for: Yang, Chunsheng1; Search for: Yang, Yubin; Search for: Lin, Fuhua; Search for: Du, Xuanmin; Search for: Ito, Takayuki |
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| Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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| Format | Text, Article |
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| Subject | case-based reasoning; ship collision avoidance; maritime affair records; case learning; case base management |
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| Abstract | With the rapid development of case-based reasoning (CBR) techniques, CBR has been widely applied to real-world applications such as collision avoidance systems. A successful CBR-based system relies on a high-quality case base, and a case creation technique for generating such a case base is highly required. In this paper, we propose an automated case learning method for CBR-based collision avoidance systems. Building on techniques from CBR and natural language processing, we developed a methodology for learning cases from maritime affair records. After giving an overview on the developed systems, we present the methodology and the experiments conducted in case creation and case evaluation. The experimental results demonstrated the usefulness and applicability of the case learning approach for generating cases from the historic maritime affair records. |
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| Publication date | 2010-12-01 |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 16335082 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | cd58ad9f-bdf5-4134-b5f9-e46d58204f4d |
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| Record created | 2010-11-10 |
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| Record modified | 2020-04-17 |
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