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Cited by 11Machine Learning and Data ClassificationEvolutionary Algorithms and ApplicationsMachine Learning and Algorithms

Dynamically Adaptive Genetic Algorithm to Select Training Data for SVMs

Kawulok, Michal, Nalepa, Jakub·2014·Lecture Notes in Computer Science
DOI 10.1007/978-3-319-12027-0_20
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Crossref · 10.1007/978-3-319-12027-0_20

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1 of 1 references verified0 citation errors · 0 not checkable

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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 [3]

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Authors
Kawulok, M., & Nalepa, J.
Affiliations
Silesian University of Technology (PL)
Corresponding author
Nalepa, Jakub
ORCID
Nalepa, Jakub
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2014
Pages
242-254
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-319-12027-0_20
OpenAlex
W1040407216
Cited by
11
References
1
Citations by year
2023: 1 · 2022: 2 · 2021: 2 · 2020: 1 · 2018: 1 · 2017: 1 · 2016: 1 · 2015: 2
FWCI
0.79
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords
training data selection, support vector machine, noisy data

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