An improved rule generation method for evidence-based classification systems Caelli, Terry and Pennington, Andrew 1993, An improved rule generation method for evidence-based classification systems, Pattern Recognition, vol. 26, no. 5, pp. 733-740, doi: 10.1016/0031-3203(93)90126-H. Attached Files Name Description MIMEType Size Downloads Title An improved rule generation method for evidence-based classification systems Author(s) Caelli, Terry orcid.org/0000-0001-9281-2556 Pennington, Andrew Journal name Pattern Recognition Volume number 26 Issue number 5 Start page 733 End page 740 Total pages 8 Publisher PERGAMON-ELSEVIER SCIENCE LTD Publication date 1993-01-01 ISSN 0031-3203 Keyword(s) Science & Technology Technology Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Computer Science Engineering CLUSTERING CLASSIFICATION RULE GENERATION EVIDENCE-BASED SYSTEMS Language eng DOI 10.1016/0031-3203(93)90126-H Indigenous content off Field of Research 0801 Artificial Intelligence and Image Processing 0806 Information Systems 0906 Electrical and Electronic Engineering HERDC Research category C1 Refereed article in a scholarly journal Persistent URL http://hdl.handle.net/10536/DRO/DU:30138563 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 08 Jun 2020, 15:43:40 EST Tue, 09 Jun 2020, 05:05:22 EST Tue, 09 Jun 2020, 06:08:12 EST Thu, 22 Oct 2020, 14:06:55 EST Thu, 22 Oct 2020, 14:08:04 EST Thu, 22 Oct 2020, 14:55:26 EST Filtered Full Citation counts:   Cited 12 times in TR Web of Science Cited 12 times in Scopus Search Google Scholar Access Statistics: 14 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Mon, 08 Jun 2020, 15:43:40 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.