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SVM-KM: speeding SVMs learning with a priori cluster selection and k-means
Abstract
A procedure called SVM-KM, based on clustering by k-means and to accelerate the training of support vector machines, is the main objective of the work. During the support vector machines (SVMs) optimization phase, training vectors near the separation margins, are likely to become support vector and must be preserved. Conversely, training vectors far from the margins are not in general taken into account for the SVM's design process. SVM-KM groups the training vectors in many clusters. Clusters formed only by a vector that belongs to the same class label can be disregard and only cluster centers are used. On the other hand, clusters with more than one class label are unchanged and all training vectors belonging to them are considered. Clusters with mixed composition are likely to happen near the separation margins and they may hold some support vectors. Consequently, the number of vectors in a SVM training is smaller and the training time can be decreased without compromising the generalization capability of the SVM.
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“For example, Reduced SVM (RSVM) [8] and Random Sampling Algorithm (RSA) [12] use a random algorithm which leads to uncertain results, while KMSVM [13,14] using clustering technology performs poorly when the classes are overlapped.”
From An efficient instance selection algorithm to reconstruct training set for support vector machine (2017) · cites this paper as [13]
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Research works printed but not linked: 16
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Cited by
An efficient instance selection algorithm to reconstruct training set for support vector machine
Liu, C., Wang, W., Wang, M., Lv, F., & Konan, M. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.10.031 · Cited by 146
Metadata
- Authors
- Barros de Almeida, M., de Padua Braga, A., & Braga, J. P.
- Affiliations
- Universidade Federal de Minas Gerais (BR)
- ORCID
- Braga, J.P.
- Venue
- Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-07
- Pages
- 162-167
- ISSN
- 1522-4899 (ISSN-L)
- OpenAlex
- W2127406992
- Cited by
- 130
- References
- 0
- Citations by year
- 2026: 1 · 2024: 6 · 2023: 4 · 2022: 3 · 2021: 4 · 2020: 2 · 2019: 6 · 2018: 6 · 2017: 5 · 2016: 6 · 2015: 9 · 2014: 10 · 2013: 8 · 2012: 9
- FWCI
- 4.34
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Brazilian Symposium on Neural Networks (SBRN) (publishedVersion)
- Funders
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- support vector machine, K-means clustering, support vector selection, cluster selection, training data reduction, model generalization
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- Barros de Almeida, M., de Padua Braga, A., & Braga, J. P. (2002). SVM-KM: speeding SVMs learning with a priori cluster selection and k-means. Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks. https://doi.org/10.1109/sbrn.2000.889732
