Search results for key=ZSS2010 :
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Refereed full papers (journals, book chapters, international conferences)
2010
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@inproceedings{ZSS2010,
vgclass = {refpap},
author = {Zaidi, Nayyar Abbas and David McG.\ Squire and David
Suter},
title = {{BoostML}: {A}n Adaptive Metric Learning for Nearest Neighbor
Classification},
booktitle = {Proceedings of the 14th Pacific-Asia Conference on
Knowledge Discovery and Data Mining},
address = {Hyderabad, India},
number = {6118},
series = {Lecture Notes in Computer Science},
pages = {142--149},
publisher = {Springer-Verlag},
month = {June~21--24},
year = {2010},
doi = {http://dx.doi.org/10.1007/978-3-642-13657-3_17},
abstract = {A Nearest Neighbor (NN) classifier assumes class
conditional probabilities to be locally smooth. This assumption is
often invalid in high dimensions and significant bias can be introduced
when using the nearest neighbor rule. This effect can be mitigated to
some extent by using a locally adaptive metric. In this work we propose
an adaptive metric learning algorithm that learns an optimal metric at
the query point. We learn a distance metric using a feature relevance
measure inspired by boosting. The modified metric results in a smooth
neighborhood that leads to better classification results. We tested our
technique on major UCI machine learning databases and compared the
results to state of the art techniques. Our method resulted in
significant improvements in the performance of the K-NN classifier and
also performed better than other techniques on major databases.},
}
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