CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2010 vol.32 Issue No.04 - April
Issue No.04 - April (2010 vol.32)
Andrea Argentini , Università di Trento, Italy
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.69
In a 2006 TPAMI paper, Wang proposed the Neighborhood Counting Measure , a similarity measure for the k-NN algorithm. In his paper, Wang mentioned the Minimum Risk Metric (MRM, ), an early distance measure based on the minimization of the risk of misclassification. Wang did not compare NCM to MRM because of its allegedly excessive computational load. In this comment paper, we complete the comparison that was missing in Wang's paper and, from our empirical evaluation, we show that MRM outperforms NCM and that its running time is not prohibitive as Wang suggested.
Pattern recognition, machine learning, k-Nearest Neighbors, distance measures, MRM, NCM.
Andrea Argentini, "About Neighborhood Counting Measure Metric and Minimum Risk Metric", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 4, pp. 763-765, April 2010, doi:10.1109/TPAMI.2009.69