Comparative Study of Distance Functions for Nearest Neighbors
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Many learning algorithms rely on distance metrics to receive their input data. Research has shown that these metrics can improve the performance of these algorithms. Over the years an often popular function is the Euclidean function. In this paper, we investigate a number of different metrics proposed by different communities, including Mahalanobis, Euclidean, Kullback-Leibler and Hamming distance. Overall, the best-performing method is the Mahalanobis distance metric.
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- Comparative Study of Distance Functions for Nearest Neighbors
- Book Title
- Advanced Techniques in Computing Sciences and Software Engineering
- pp 79-84
- Print ISBN
- Online ISBN
- Springer Netherlands
- Copyright Holder
- Springer Science+Business Media B.V.
- Additional Links
- Kullback-Leibler distance
- Euclidean distance
- Mahalanobis distance
- Manhattan distance
- Hamming distance
- Minkowski distance
- Nearest Neighbor
- Industry Sectors
- eBook Packages
- Khaled Elleithy (ID1)
- Editor Affiliations
- ID1. School of Engineering, University of Bridgeport
- Author Affiliations
- 1. School of Computing and Information Technology, University of Technology, Kingston 6, Jamaica W.I
- 2. Department of Mathematics and Computing Centre for Systems Biology, University of Southern Queensland, Toowoomba, Australia
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