Cited by 71Machine Learning and Data ClassificationMachine Learning and AlgorithmsStochastic Gradient Optimization Techniques

A Random Sampling Technique for Training Support Vector Machines: For Primal-Form Maximal-Margin Classifiers

Balcázar, Jose, Dai, Yang, Watanabe, Osamu·2001·Lecture Notes in Computer Science

Integrity signals

Registry

Crossref · 10.1007/3-540-45583-3_11

Cites

16 of 16 references verified0 citation errors · 0 not checkable

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Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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.”
  • 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.”

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Authors
Balcázar, J., Dai, Y., & Watanabe, O.
Affiliations
Tokyo Institute of Technology (JP)
Venue
Lecture Notes in Computer Science
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
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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