Multi-class logistic regression classifier with BFGS method

Xiao Bo Jin, Feng Wang, Peng Fei Zhu, Jun Wei Yu

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


The classical multi-class logistic regression classifier uses Newton method to optimize its loss function and suffers the expensive computations and the un-stable iteration process. In our work, we apply the state-of-art optimization techniques BFGS to train multi-class logistic regression and compare them with Newton method on the classification accuracy of 25 datasets experimentally. The results show that BFGS achieves better classification accuracy than the Newton method. Moreover, BFGS have the lower time complexity, in contrast with Newton method. Finally, we also observe that logistic classifier with BFGS method demonstrate comparable performance with the SVM classifier.

Original languageEnglish
Title of host publicationMechatronics and Industrial Informatics
Number of pages5
Publication statusPublished - 2013
Externally publishedYes
Event2013 International Conference on Mechatronics and Industrial Informatics, ICMII 2013 - Guangzhou, China
Duration: 13 Mar 201314 Mar 2013

Publication series

NameApplied Mechanics and Materials
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482


Conference2013 International Conference on Mechatronics and Industrial Informatics, ICMII 2013


  • BFGS
  • Multi-class logistic regression
  • Newton method

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