Book ChapterDOI
Comparative study of distance functions for nearest neighbors
Janett Walters-Williams,Yan Li +1 more
- pp 79-84
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This paper investigates a number of different metrics proposed by different communities, including Mahalanobis, Euclidean, Kullback-Leibler and Hamming distance, and concludes that the best-performing method is the MahalanOBis distance metric.Abstract:
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 metricread more
Citations
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Doppler Radar Fall Activity Detection Using the Wavelet Transform
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Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets
TL;DR: Several similarity measures have been defined based on a combination between well-known distances for both numerical and binary data, and to investigate the performance of k-NN on heterogeneous datasets, where data can be described as a mixture of numerical and categorical features.
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Machine Learning in Aerodynamic Shape Optimization
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Efficient k-nearest neighbor search based on clustering and adaptive k values
TL;DR: The caKD+ algorithm is introduced, which tackles the inefficiency of the k -Nearest Neighbor algorithm by combining the use of feature learning techniques, clustering methods, adaptive search parameters per cluster, and theUse of pre-calculated K-Dimensional Tree structures, and results in a highly efficient search method.
Journal ArticleDOI
Property-based biomass feedstock grading using k-Nearest Neighbour technique
Obafemi O. Olatunji,Stephen A. Akinlabi,Stephen A. Akinlabi,Nkosinathi Madushele,Paul A. Adedeji +4 more
TL;DR: The implementation of the k-Nearest Neighbour (k-NN) classification model shows that k–NN can serve as a handy tool for biomass resources classification irrespective of the sources and origins.
References
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Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Proceedings Article
Distance Metric Learning for Large Margin Nearest Neighbor Classification
TL;DR: In this article, a Mahanalobis distance metric for k-NN classification is trained with the goal that the k-nearest neighbors always belong to the same class while examples from different classes are separated by a large margin.
Proceedings ArticleDOI
Information-theoretic metric learning
TL;DR: An information-theoretic approach to learning a Mahalanobis distance function that can handle a wide variety of constraints and can optionally incorporate a prior on the distance function and derive regret bounds for the resulting algorithm.
Journal ArticleDOI
Improved heterogeneous distance functions
D. Randall Wilson,Tony Martinez +1 more
TL;DR: This article proposed three new heterogeneous distance functions, called the Heterogeneous Value Difference Metric (HVDM), the Interpolated Value Difference metric (IVDM), and the Windowed Value Difference measure (WVDM) to handle applications with nominal attributes, continuous attributes and both.
Book
Encyclopedia of Measurement and Statistics
TL;DR: The Encyclopedia is specifically written to appeal to undergraduate students as well as practitioners, researchers and consumers of information, and provides coverage of every major facet of these two different, but highly integrated disciplines with reference to mean, mode and median.