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Neighborhood Counting Measure and Minimum Risk Metric

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Wang, H (2010) Neighborhood Counting Measure and Minimum Risk Metric. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (4). pp. 766-768. [Journal article]

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DOI: 10.1109/TPAMI.2010.16

Abstract

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 about the time complexity of MRM, and presents an experimental comparison of MRM and NCM. This paper addresses the questions raised 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.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:11290
Deposited By:Professor Hui Wang
Deposited On:12 Apr 2010 17:24
Last Modified:12 Apr 2010 17:24

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