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Abstract: In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of the feature space is required. We introduce a nearest neighbor algorithm for learning in domains with symbolic features. Our algorithm calculates distance tables that allow it to produce real-valued... (Update)
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BibTeX entry: (Update)
Cost, S., & Salzberg, S. (1993). A weighted nearest neighbor algorithm for learning with symbolic features. Machine Learning, 10, 57--78. http://citeseer.ist.psu.edu/cost93weighted.html More
@article{ cost93weighted,
author = "Scott Cost and Steven Salzberg",
title = "A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features",
journal = "Machine Learning",
volume = "10",
pages = "57-78",
year = "1993",
url = "citeseer.ist.psu.edu/cost93weighted.html" }
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