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How to learn distances from categorical variables (nominal attributes) is a key problem in instance-based learning and other paradigms of machine learning. Recent work in distance learning has shown that a surprisingly simple Value Difference Metric (VDM), with strong assumptions of independence among attributes, is competitive with state-of-the-art distance functions such as Short and Fukunaga Metric...
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