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Statistical Learning From Non-Metric Data Anonymous Author(s) Affiliation



Address email Non-metric pairwise proximity data are frequently encountered in practical applications. Although practitioners use various ways of adjustments and corrections, a complete and well-sounded statistical learning theory applicable to such data is still missing. Machine learning would greatly benefit from methods that could generalize from settings close to human perception, or otherwise non-metric. In this paper, we provide both theoretical and experimental evidence about the existence of such methods. This enables the foundation needed to define statistical learning from non-metric data

Topics: Similarity and Distance Learning, Learning with Structured Data
Year: 2008
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