An Extension of Semi-supervised Boosting to Multi-valued Classification Problems

Article Properties
  • Language
    English
  • DOI (url)
    10.17929/tqs.6.60
  • Publication Date
    2021/02/18
  • Journal
    Total Quality Science
  • Indian UGC (journal)
  • Refrences
    19
  • Yuta Sakai Waseda University
  • Kazuki Yasui Waseda University
  • Kenta Mikawa Shonan Institute of Technology
  • Masayuki Goto Waseda University
Cite
Sakai, Yuta, et al. “An Extension of Semi-Supervised Boosting to Multi-Valued Classification Problems”. Total Quality Science, vol. 6, no. 2, 2021, pp. 60-69, https://doi.org/10.17929/tqs.6.60.
Sakai, Y., Yasui, K., Mikawa, K., & Goto, M. (2021). An Extension of Semi-supervised Boosting to Multi-valued Classification Problems. Total Quality Science, 6(2), 60-69. https://doi.org/10.17929/tqs.6.60
Sakai Y, Yasui K, Mikawa K, Goto M. An Extension of Semi-supervised Boosting to Multi-valued Classification Problems. Total Quality Science. 2021;6(2):60-9.
Refrences
Title Journal Journal Categories Citations Publication Date
10.1201/b12207
9 Joachims, T., (2003), "Transductive Learning via Spectral Graph Partitioning", ICML'03 Proceedings of the Twentieth International Conference on International Conference on Machine Learning, pp. 290-297.
8 Dietterich, T. and Bakiri, G., (1995), "Solving Multiclass Learning Problems via Error Correcting Output Codes", Journal of Artificial Intelligence Research, Vol. 2, No. 1, pp. 263-286.
7 Cortes, C. and Vapnik, V., (1995), "Support-Vector Networks", Machine Learning, Vol. 20, No. 3, pp. 273-297.
6 Chapelle, O., Scholkopf, B. and Zien, A., (2009), "Semi-supervised Learning", IEEE Transactions on Neural Networks, pp. 542-542.