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To appear in Proceedings of IEEE Workshop on Learning in Computer Vision and Pattern Recognition, June 2005. Boosting Nearest Neighbor Classifiers for Multiclass Recognition
 

Summary: To appear in Proceedings of IEEE Workshop on Learning in Computer Vision and Pattern Recognition, June 2005.
Boosting Nearest Neighbor Classifiers for Multiclass Recognition
Vassilis Athitsos and Stan Sclaroff
Computer Science Department
Boston University
111 Cummington Street
Boston, MA 02215
email: {athitsos, sclaroff}@cs.bu.edu
Abstract
Nearest neighbor classifiers are a popular method for mul-
ticlass recognition in a wide range of computer vision and
pattern recognition domains. At the same time, the accu-
racy of nearest neighbor classifiers is sensitive to the choice
of distance measure. This paper introduces an algorithm
that uses boosting to learn a distance measure for multi-
class k-nearest neighbor classification. Given a family of
distance measures as input, AdaBoost is used to learn a
weighted distance measure, that is a linear combination
of the input measures. The proposed method can be seen
both as a novel way to learn a distance measure from data,

  

Source: Athitsos, Vassilis - Department of Computer Science and Engineering, University of Texas at Arlington

 

Collections: Computer Technologies and Information Sciences