A Fast k-Nearest Neighbor Classifier Using Unsupervised Clustering-Reference-Cited by-同舟云学术

A Fast k-Nearest Neighbor Classifier Using Unsupervised Clustering

Published:2017 Issue: Volume: Page:185-193
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
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Author:

Vajda Szilárd,Santosh K. C.

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-10-4859-3_17

Reference22 articles.

1. Beis, J.S., Lowe, D.G.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, CVPR 1997, p. 1000. IEEE Computer Society, Washington, DC (1997)

2. Bentley, J.L.: Multidimensional divide-and-conquer. Commun. ACM 23(4), 214–229 (1980)

3. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press Inc., New York (1995)

4. Chen, J., Fang, H.R., Saad, Y.: Fast approximate kNN graph construction for high dimensional data via recursive Lanczos bisection. J. Mach. Learn. Res. 10, 1989–2012 (2009)

5. Ciresan, D.C., Meier, U., Gambardella, L.M., Schmidhuber, J.: Convolutional neural network committees for handwritten character classification. In: ICDAR, pp. 1135–1139 (2011)

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