A novel approach for the hyperparameters of support vectorregression
Jing-Tsong Jeng
Chen-Chia Chuang
Dept. of Electron. Eng., Hwa-Hsia Coll. of Technol. & Commerce, Chung-Ho City;
This paper appears in: Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Publication Date: 2002
Volume: 1,
On page(s): 642-647
Meeting Date: 05/12/2002 - 05/17/2002
Location: Honolulu, HI, USA
ISBN: 0-7803-7278-6
References Cited: 17
INSPEC Accession Number: 7327689
Digital Object Identifier: 10.1109/IJCNN.2002.1005547
Current Version Published: 2002-08-07
Abstract
In order to determine the hyperparameters of support vector
regression (SVR), an approach with a two structured method is proposed
to determine the kernel parameter σ and ε in the
ε-insensitive loss function. Firstly, the kernel parameter σ
of a Gaussian kernel function is determined by the competitive
agglomeration (CA) clustering algorithm. The CA clustering algorithm
incorporates the advantage of both hierarchical and partitioned
clustering algorithms. Besides, it can find the nearly "optimum" number
of clusters as well as its center of clusters in the clustering process.
Secondly, the repeated SVR approach is proposed to obtain a proper
ε in the ε-insensitive loss function that can be included in
most of the data. Based on the efficiently structured way for choosing
the hyperparameters σ and ε, the simulation results have
shown that the proposed approach comes close to the "optimum"
hyperparameter region
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