@inproceedings{edceb9546b4845549d06c6b854f54c20,
title = "Improvements in K-nearest neighbor classification",
abstract = "We have deveioped two novel methods to improve K-nearest neighbor (K-NN) classifications. First, we introduce a new technique to greatly reduce the template size. This significantly improves classification time with no accuracy drop. Secondly, we introduce a preprocessing procedure to preclude a large part of prototype patterns which are unlikely to match the unknown pattern. This again accelerates the classification procedure considerably. The simulation results on the GSC digit recognizer [1] show that the accommodation of two procedures to K-NN search achieves 7 times faster than the original one without any decay in classification accuracy.",
author = "Yingquan Wu and Ianakiev, \{Krasimir G.\} and Venu Govindaraju",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 2nd International Conference on Advances in Pattern Recognition, ICAPR 2001 ; Conference date: 11-03-2001 Through 14-03-2001",
year = "2001",
language = "English",
isbn = "3540417672",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "222--229",
editor = "Sameer Singh and Nabeel Murshed and Walter Kropatsch",
booktitle = "Advances in Pattern Recognition - 2nd International Conference, ICAPR 2001, Proceedings",
address = "Germany",
}