JACIII Vol.19 No.1 pp. 51-57
doi: 10.20965/jaciii.2015.p0051


Semi-Supervised Sequential Kernel Regression Models with Penalty Functions

Hengjin Tang, Sadaaki Miyamoto, and Yasunori Endo

Department of Risk Engineering, School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan

April 20, 2014
August 25, 2014
January 20, 2015
kernel regression, switching regression models, semi-supervised clustering, sequential clustering, penalty functions
Switching regression models can output multiple clusters and regression models. However, there is one problem: the results have a strong dependency on the predefined number of clusters. To avoid these drawbacks, we have researched sequential extractions. In sequential extractions process, one cluster is extracted at a time using a method of noise-detection, and the number of clusters are determined automatically. We propose semi-supervised sequential kernel regression models with penalty functions. Additionally, we also find that the sensitivity against the regularization parameter λ can be alleviated by semi-supervisions using penalty functions. We show the effectiveness of the proposed method by using numerical examples.
Cite this article as:
H. Tang, S. Miyamoto, and Y. Endo, “Semi-Supervised Sequential Kernel Regression Models with Penalty Functions,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.1, pp. 51-57, 2015.
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