CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2010 vol.32 Issue No.04 - April
Issue No.04 - April (2010 vol.32)
Hui Wang , University of Ulster, Jordanstown, Newtownabbey, Co. Antrim
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.16
The neighborhood counting measure (NCM) is a similarity measure based on the counting of all common neighborhoods in a data space . The minimum risk metric (MRM)  is a distance measure based on the minimization of the risk of misclassification. The paper by Argentini and Blanzieri  refutes a remark in  about the time complexity of MRM, and presents an experimental comparison of MRM and NCM. This paper is a response to the paper by Argentini and Blanzieri . The original remark is clarified by a combination of theoretical analysis of different implementations of MRM and experimental comparison of MRM and NCM using straightforward implementations of the two measures.
Minimum risk metric, neighborhood counting measure, k-nearest neighbor.
Hui Wang, "Neighborhood Counting Measure and Minimum Risk Metric", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 4, pp. 766-768, April 2010, doi:10.1109/TPAMI.2010.16