Search results for key=ZSS2010 : 1 match found.

2010

• @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.},
  }