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Unsupervised Learning Using Multivariate Symbolic Hybrid
New York, New York June 26-June 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CBMS.2003.121281716th IEEE Symposium on Computer-Based ...
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Enis Avdičaušević, University of Maribor
Mitja Lenič, University of Maribor
Peter Kokol, University of Maribor
One of the most challenging Tasks in the area of knowledge discovery is to express learned knowledge in a form, which can be understood by domain experts (e.g. medical experts). In the paper we present our approach to unsupervised learning using multivariate symbolic hybrid. Main advantage of multimethod symbolic hybrid is that learned knowledge is expressed in a form of symbolic rules. Learned knowledge is much more understandable to domain experts, which increases its value and makes it much easier to apply.
Citation:
Enis Avdičaušević, Mitja Lenič, Peter Kokol, "Unsupervised Learning Using Multivariate Symbolic Hybrid," cbms, pp.373, 16th IEEE Symposium on Computer-Based Medical Systems (CBMS'03), 2003
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