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A Batch Learning Vector Quantization Algorithm for Nearest Neighbour Classification

Authors:

Sergio Bermejo,

Joan CabestanyAuthors Info & Claims

Neural Processing Letters, Volume 11, Issue 3

Pages 173 - 184

https://doi.org/10.1023/A:1009634824627

Published: 01 June 2000 Publication History

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Abstract

We introduce a batch learning algorithm to design the set of prototypes of 1 nearest-neighbour classifiers. Like Kohonen's LVQ algorithms, this procedure tends to perform vector quantization over a probability density function that has zero points at Bayes borders. Although it differs significantly from their online counterparts since: (1) its statistical goal is clearer and better defined; and (2) it converges superlinearly due to its use of the very fast Newton's optimization method. Experiments results using artificial data confirm faster training time and better classification performance than Kohonen's LVQ algorithms.

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Fernández FIsasi P(2019)Evolutionary Design of Nearest Prototype ClassifiersJournal of Heuristics10.1023/B:HEUR.0000034715.70386.5b10:4(431-454)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1023/B%3AHEUR.0000034715.70386.5b
Bello MNápoles GVanhoof KBello R(2019)Prototypes Generation from Multi-label Datasets Based on Granular ComputingProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-33904-3_13(142-151)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-33904-3_13
Fernández FIsasi P(2018)Nearest prototype classification of noisy dataArtificial Intelligence Review10.1007/s10462-009-9116-730:1-4(53-66)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10462-009-9116-7
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Index Terms

A Batch Learning Vector Quantization Algorithm for Nearest Neighbour Classification
1. Computing methodologies
  1. Machine learning
2. Mathematics of computing
  1. Probability and statistics

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Published In

Neural Processing Letters Volume 11, Issue 3

June 2000

65 pages

ISSN:1370-4621

Editors:
François Blayo
Prefigure, Lyon, France
,
Mohamad H. Hassoun
Wayne State Univ., Detroit, MI
,
Michel Verleysen
Univ. Catholique de Louvain, Louvain-la-Neuve, Belgium

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Kluwer Academic Publishers

United States

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Published: 01 June 2000

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Fernández FIsasi P(2019)Evolutionary Design of Nearest Prototype ClassifiersJournal of Heuristics10.1023/B:HEUR.0000034715.70386.5b10:4(431-454)Online publication date: 1-Jun-2019
https://dl.acm.org/doi/10.1023/B%3AHEUR.0000034715.70386.5b
Bello MNápoles GVanhoof KBello R(2019)Prototypes Generation from Multi-label Datasets Based on Granular ComputingProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications10.1007/978-3-030-33904-3_13(142-151)Online publication date: 28-Oct-2019
https://dl.acm.org/doi/10.1007/978-3-030-33904-3_13
Fernández FIsasi P(2018)Nearest prototype classification of noisy dataArtificial Intelligence Review10.1007/s10462-009-9116-730:1-4(53-66)Online publication date: 28-Dec-2018
https://dl.acm.org/doi/10.1007/s10462-009-9116-7
Yang CYang YLiu J(2012)Model fusion-based batch learning with application to oil spills detectionProceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence10.1007/978-3-642-31087-4_5(40-47)Online publication date: 9-Jun-2012
https://dl.acm.org/doi/10.1007/978-3-642-31087-4_5

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Modified nearest neighbour classifier for hyperspectral data classification

Regression Learning Vector Quantization

A review of learning vector quantization classifiers

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