@inproceedings{e64c886a76f64dc390fd450849f5d622,
title = "Formalising the knowledge content of case memory systems",
abstract = "Discussions of case-based reasoning often reflect an implicit assumption that a case memory system will become better informed, i.e. will increase in knowledge, as more cases are added to the case-base. This paper considers formalisations of this 'knowledge content' which are a necessary preliminary to more rigourous analysis of the performance of case-based reasoning systems. In particular we are interested in modelling the learning aspects of case-based reasoning in order to study how the performance of a case-based reasoning system changes as it accumulates problem-solving experience. The current paper presents a {\textquoteleft}case-base semantics{\textquoteright} which generalises recent formalisations of casebased classification. Within this framework, the paper explores various issues in assuring that these semantics are well-defined, and illustrates how the knowledge content of the case memory system can be seen to reside in both the chosen similarity measure and in the cases of the casebase.",
author = "Griffiths, \{A. D.\} and Bridge, \{D. G.\}",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 1995.; 1st United Kingdom Workshop on Case-Based Reasoning, CBR 1995 ; Conference date: 12-01-1995 Through 12-01-1995",
year = "1995",
doi = "10.1007/3-540-60654-8\_20",
language = "English",
isbn = "3540606548",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "32--41",
editor = "Watson, \{Ian D.\}",
booktitle = "Progress in Case-Based Reasoning - 1st United Kingdom Workshop, Proceedings",
address = "Germany",
}