Neighborhood Counting Measure and Minimum Risk Metric

Research output: Contribution to journalArticle

5 Citations (Scopus)

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.
LanguageEnglish
Pages766-768
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume32
Issue number4
DOIs
Publication statusPublished - Apr 2010

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Counting
Metric
Misclassification
Distance Measure
Similarity Measure
Time Complexity
Theoretical Analysis

Cite this

Wang, H. / Neighborhood Counting Measure and Minimum Risk Metric. 2010 ; Vol. 32, No. 4. pp. 766-768.
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Neighborhood Counting Measure and Minimum Risk Metric. / Wang, H.

Vol. 32, No. 4, 04.2010, p. 766-768.

Research output: Contribution to journalArticle

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