Literature DB >> 18244425

Asymptotic convergence of an SMO algorithm without any assumptions.

Chih-Jen Lin1.   

Abstract

The asymptotic convergence of C.-J. Lin (2001) can be applied to a modified SMO (sequential minimal optimization) algorithm by S.S. Keerthi et al. (2001) with some assumptions. The author shows that for this algorithm those assumptions are not necessary.

Year:  2002        PMID: 18244425     DOI: 10.1109/72.977319

Source DB:  PubMed          Journal:  IEEE Trans Neural Netw        ISSN: 1045-9227


  1 in total

1.  DPWSS: differentially private working set selection for training support vector machines.

Authors:  Zhenlong Sun; Jing Yang; Xiaoye Li; Jianpei Zhang
Journal:  PeerJ Comput Sci       Date:  2021-12-01
  1 in total

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