Structured sparse support vector machine with ordered features

Article Properties
  • Language
    English
  • DOI (url)
    10.1080/02664763.2020.1849053
  • Publication Date
    2020/11/18
  • Journal
    Journal of Applied Statistics
  • Indian UGC (journal)
  • Refrences
    29
  • Citations
    1
  • Kuangnan Fang Department of Statistics, School of Economics, Xiamen University, Xiamen, Fujian, ChinaKey Laboratory of Econometrics, Ministry of Education, Xiamen University, Xiamen, Fujian, China
  • Peng Wang Department of Statistics, School of Economics, Xiamen University, Xiamen, Fujian, China
  • Xiaochen Zhang Department of Statistics, School of Economics, Xiamen University, Xiamen, Fujian, China
  • Qingzhao Zhang Department of Statistics, School of Economics, Xiamen University, Xiamen, Fujian, ChinaKey Laboratory of Econometrics, Ministry of Education, Xiamen University, Xiamen, Fujian, ChinaThe Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian, China
Cite
Fang, Kuangnan, et al. “Structured Sparse Support Vector Machine With Ordered Features”. Journal of Applied Statistics, vol. 49, no. 5, 2020, pp. 1105-20, https://doi.org/10.1080/02664763.2020.1849053.
Fang, K., Wang, P., Zhang, X., & Zhang, Q. (2020). Structured sparse support vector machine with ordered features. Journal of Applied Statistics, 49(5), 1105-1120. https://doi.org/10.1080/02664763.2020.1849053
Fang, Kuangnan, Peng Wang, Xiaochen Zhang, and Qingzhao Zhang. “Structured Sparse Support Vector Machine With Ordered Features”. Journal of Applied Statistics 49, no. 5 (2020): 1105-20. https://doi.org/10.1080/02664763.2020.1849053.
Fang K, Wang P, Zhang X, Zhang Q. Structured sparse support vector machine with ordered features. Journal of Applied Statistics. 2020;49(5):1105-20.
Journal Categories
Science
Mathematics
Science
Mathematics
Probabilities
Mathematical statistics
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Citations
Title Journal Journal Categories Citations Publication Date
Machine learning model based on non-convex penalized huberized-SVM Journal of Electronic Science and Technology
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering
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Citations Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science1
Technology: Electrical engineering. Electronics. Nuclear engineering1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Machine learning model based on non-convex penalized huberized-SVM and was published in 2024. The most recent citation comes from a 2024 study titled Machine learning model based on non-convex penalized huberized-SVM. This article reached its peak citation in 2024, with 1 citations. It has been cited in 1 different journals, 100% of which are open access. Among related journals, the Journal of Electronic Science and Technology cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year