A Random Sampling Technique for Training Support Vector Machines: For Primal-Form Maximal-Margin Classifiers
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RSVM and RSA use a random algorithm, which leads to uncertain results.
“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 [12]
Random sampling algorithms' performance is uncertain because some support vectors may not be included in the randomly selected subset.
“Although their experiments show that these methods are faster than the original SVM, the performance of these randomly sampling algorithms is uncertain since some support vectors may not be included in the randomly selected subset.”
From An efficient instance selection algorithm to reconstruct training set for support vector machine (2017) · cites this paper as [12]
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- Authors
- Balcázar, J., Dai, Y., & Watanabe, O.
- Affiliations
- Tokyo Institute of Technology (JP)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2001
- Pages
- 119-134
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1705336121
- Cited by
- 71
- References
- 16
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 5 · 2022: 3 · 2021: 8 · 2020: 1 · 2019: 2 · 2018: 2 · 2016: 3 · 2015: 5 · 2014: 6 · 2013: 3 · 2012: 8
- FWCI
- 1.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- 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 › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- support vector machine, random sampling, binary classification, outlier detection, combinatorial optimization, training algorithms, primal optimization
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