This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
About Neighborhood Counting Measure Metric and Minimum Risk Metric
April 2010 (vol. 32 no. 4)
pp. 763-765
ASCII Text
x 
 
Andrea Argentini, Enrico Blanzieri, "About Neighborhood Counting Measure Metric and Minimum Risk Metric," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 763-765, April, 2010.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2009.69,
author = {Andrea Argentini and Enrico Blanzieri},
title = {About Neighborhood Counting Measure Metric and Minimum Risk Metric},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {32},
number = {4},
issn = {0162-8828},
year = {2010},
pages = {763-765},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.69},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - About Neighborhood Counting Measure Metric and Minimum Risk Metric
IS - 4
SN - 0162-8828
SP763
EP765
EPD - 763-765
A1 - Andrea Argentini,
A1 - Enrico Blanzieri,
PY - 2010
KW - Pattern recognition
KW - machine learning
KW - k-Nearest Neighbors
KW - distance measures
KW - MRM
KW - NCM.
VL - 32
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
Andrea Argentini, Università di Trento, Italy
Enrico Blanzieri, Università di Trento, Italy
In a 2006 TPAMI paper, Wang proposed the Neighborhood Counting Measure [2], a similarity measure for the k-NN algorithm. In his paper, Wang mentioned the Minimum Risk Metric (MRM, [1]), 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.

[1] E. Blanzieri and F. Ricci, "A Minimum Risk Metric for Nearest Neighbor Classification," Proc. 16th Int'l Conf. Machine Learning, pp. 22-31, 1999.
[2] H. Wang, "Nearest Neighbors by Neighborhood Counting," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 6, pp. 942-953, June 2006, http://doi. ieeecomputersociety.org/10.1109 TPAMI.2006.126
[3] S. Mahamud and M. Hebert, "Minimum Risk Distance Measure for Object Recognition," Proc. Int'l Conf. Computer Vision, pp. 242-248, 2003, http://csdl.computer.org/comp/proceedings/ iccv/2003/1950/01195010242.
[4] H. Wang, "Personal communication," 2007.
[5] I.H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, second ed. Morgan Kaufmann, 2005.
[6] A. Argentini and E. Blanzieri, "Neighborhood Counting Measure Metric and Minimum Risk Metric: An Empirical Comparison," Technical Report DISI-08-057, DISI, Univ. of Trento Italy, 2008.

Index Terms:
Pattern recognition, machine learning, k-Nearest Neighbors, distance measures, MRM, NCM.
Citation:
Andrea Argentini, Enrico Blanzieri, "About Neighborhood Counting Measure Metric and Minimum Risk Metric," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 763-765, April 2010, doi:10.1109/TPAMI.2009.69
Usage of this product signifies your acceptance of the Terms of Use.