DocumentCode
1241578
Title
About Neighborhood Counting Measure Metric and Minimum Risk Metric
Author
Argentini, A. ; Blanzieri, Enrico
Author_Institution
Dipt. di Ing. e Scienza dell´Inf., Univ. di Trento, Trento, Italy
Volume
32
Issue
4
fYear
2010
fDate
4/1/2010 12:00:00 AM
Firstpage
763
Lastpage
765
Abstract
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.
Keywords
learning (artificial intelligence); pattern classification; early distance measure; k-NN algorithm; k-nearest neighbor algorithm; machine learning; minimum risk metric; neighborhood counting measure metric; pattern recognition; risk minimization; MRM; NCM.; Pattern recognition; distance measures; k-Nearest Neighbors; machine learning;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2009.69
Filename
4815257
Link To Document