Abstract
There are applications of case-like knowledge where, on the one hand, no obvious best way to structure the material exists, and on the other, the number of cases is not large enough for machine learning to find regularities that can be used for structuring. Numerical taxonomy is proposed as a technique for determining degrees of similarity between cases under these conditions. Its effect is illustrated in a novel application for case-like knowledge: authentication of paintings. © 2008 Springer Berlin Heidelberg.
Cite
CITATION STYLE
Silva, L. A. L., Campbell, J. A., Eastaugh, N., & Buxton, B. F. (2008). A case for numerical taxonomy in case-based reasoning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5249 LNAI, pp. 177–186). Springer Verlag. https://doi.org/10.1007/978-3-540-88190-2_23
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