Discriminant Analysis via Smoothly Varying Regularization

Hisao Yoshida, Shuichi Kawano, Yoshiyuki Ninomiya

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The discriminant method, which uses a basis expansion in the logistic regression model and estimates it by a simply regularized likelihood, is considerably efficient especially when the discrimination boundary is complex. However, when the complexities of the boundary are different by region, the method tends to cause under-fitting or/and over-fitting at some regions. To overcome this difficulty, a smoothly varying regularization is proposed in the framework of the logistic regression. Through simulation studies based on synthetic data, the superiority of the proposed method to some existing methods is checked.

Original languageEnglish
Title of host publicationIntelligent Decision Technologies - Proceedings of the 13th KES-IDT 2021 Conference
EditorsIreneusz Czarnowski, Robert J. Howlett, Lakhmi C. Jain
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9789811627644
Publication statusPublished - 2021
Externally publishedYes
Event13th International KES Conference on Intelligent Decision Technologies, KES-IDT 2021 - Virtual, Online
Duration: Jun 14 2021Jun 16 2021

Publication series

NameSmart Innovation, Systems and Technologies
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026


Conference13th International KES Conference on Intelligent Decision Technologies, KES-IDT 2021
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Computer Science(all)


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