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| Content Provider | ACM Digital Library |
|---|---|
| Author | Nonn, Éva Murbach, Ruth |
| Abstract | We focus on one of the central concepts of case-based reasoning: similarity. In the field of sentencing, where the really decided cases are often on the harder side, similarity is multidimensional and depends less on formal rules than on various legitimate principles, objectives and factors which relate to the offender, the victim, the act and its social context. The paper presents our data base of empirically analysed cases of fraud and discusses two of the different phases completed to reduce complexity: (1) a decision topology whose categories emerged from in-depth interviews with judges, Crown and defense attorneys and which proved to be statistically significant; (2) experimenting with FXS, a program designed to retrieve similar cases based on their degree of salience. The salience coefficient used is a measure of similarity based on relative high or low frequency of factors in their local context. Particularly promising, although not yet formally explained, are FXS' flexible weighting possibilities. |
| Starting Page | 236 |
| Ending Page | 244 |
| Page Count | 9 |
| File Format | |
| ISBN | 0897916069 |
| DOI | 10.1145/158976.159006 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 1993-08-01 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
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