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Title: Combining adaptive vector quantization and prototype selection techniques to improve nearest neighbour classifiers (English)
Author: Ferri, Francesc J.
Language: English
Journal: Kybernetika
ISSN: 0023-5954
Volume: 34
Issue: 4
Year: 1998
Pages: [405]-410
Summary lang: English
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Category: math
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Summary: Prototype Selection (PS) techniques have traditionally been applied prior to Nearest Neighbour (NN) classification rules both to improve its accuracy (editing) and to alleviate its computational burden (condensing). Methods based on selecting/discarding prototypes and methods based on adapting prototypes have been separately introduced to deal with this problem. Different approaches to this problem are considered in this paper and their main advantages and drawbacks are pointed out along with some suggestions for their joint application in some cases. (English)
Keyword: nearest neighbour classification
Keyword: prototype selection
MSC: 62H30
MSC: 68P30
MSC: 68T10
MSC: 68U10
idZBL: Zbl 1274.68382
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Date available: 2009-09-24T19:18:01Z
Last updated: 2015-03-28
Stable URL: http://hdl.handle.net/10338.dmlcz/135223
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Reference: [8] Kohonen T., Kangas J., Laaksonen J., Torkkola K.: Lvq_pak: The Learning Vector Quantization Program Package.Technical Report, Helsinki Univ. of Tech., 1992
Reference: [9] Kraaijveld M. A., Duin R. P. W.: On backpropagation learning of edited data sets.In: Proc. of the Int. Neural Network Conf., 1990, pp. 741–744
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Reference: [11] Wilson D. L.: Asymptotic properties of nearest neighbor rules using edited data.IEEE Trans. Systems Man Cybernet. 2 (1972), 3, 408–421 Zbl 0276.62060, MR 0329139, 10.1109/TSMC.1972.4309137
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