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Cited by 49Machine Learning and Data ClassificationMachine Learning and AlgorithmsStochastic Gradient Optimization Techniques

Fast data selection for SVM training using ensemble margin

Guo, Li, Boukir, Samia·2015·Pattern Recognition Letters
DOI 10.1016/j.patrec.2014.08.003
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  • Training SVM on large datasets is inefficient or slow and a bottleneck because of high computational complexity (QP time O(n^3) and space O(n^2)) and model complexity.

    “However, it is inefficient or impracticable to implement support vector machine in dealing with large scale training set due to its computational difficulties as well as the model complexity.”

    From An efficient instance selection algorithm to reconstruct training set for support vector machine (2017) · cites this paper as [4]

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Authors
Guo, L., & Boukir, S.
Affiliations
Centre National de la Recherche Scientifique (FR)
Corresponding author
Guo, Li
ORCID
Venue
Pattern Recognition Letters
Publisher
Elsevier BV
Published
2015-01
Volume
51
Pages
112-119
ISSN
0167-8655 (ISSN-L)
DOI
10.1016/j.patrec.2014.08.003
OpenAlex
W2047300736
Cited by
49
References
1
Citations by year
2026: 3 · 2025: 2 · 2024: 3 · 2023: 2 · 2022: 4 · 2021: 6 · 2020: 3 · 2019: 7 · 2018: 6 · 2017: 8 · 2016: 2 · 2015: 3
FWCI
4.32
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
Pattern Recognition Letters (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
  • Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
Keywords
support vector machine training, data selection, instance selection, training complexity reduction, large-scale classification

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