@inproceedings{c58bcd67bcaa46deb5d3527f154ea85f,
title = "Discriminant Analysis via Smoothly Varying Regularization",
abstract = "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.",
keywords = "Basis expansion, Boundary smoothness, Logistic regression, Over-fitting, Regularization, Under-fitting",
author = "Hisao Yoshida and Shuichi Kawano and Yoshiyuki Ninomiya",
note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 13th International KES Conference on Intelligent Decision Technologies, KES-IDT 2021 ; Conference date: 14-06-2021 Through 16-06-2021",
year = "2021",
doi = "10.1007/978-981-16-2765-1_37",
language = "English",
isbn = "9789811627644",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "441--455",
editor = "Ireneusz Czarnowski and Howlett, {Robert J.} and Jain, {Lakhmi C.}",
booktitle = "Intelligent Decision Technologies - Proceedings of the 13th KES-IDT 2021 Conference",
address = "Germany",
}