NDLI: A Modified Generalized RBF Model with EM-based Learning Algorithm for Medical Applications

Content Provider	IEEE Xplore Digital Library
Author	Ma Li Wahab A. Quek Chai
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Eng., Nanyang Technol. Univ. (Ma Li; Wahab A; Quek Chai)
Abstract	Radial basis function (RBF) has been widely used in different fields, due to its fast learning and interpretability of its solution. One problem of classical RBF is that it suffers from curse of dimensionality that the number of basis functions would explode with the increase of dimensions in the dataset. This explosion usually impairs the usefulness and interpretability of RBF, especially in medical applications, where the dimensions of dataset are high and the explanations of solutions are important. In this paper, we propose a generalized RBF (GRBF) model to reduce the number of basis functions and thus alleviate curse of dimensionality. An EM-based training algorithm is also introduced, which uses fewer parameters compared to some classical supervised learning methods. This would make the learning process simpler and more convenient in practice. Moreover, GRBF trained by the new algorithm has an apparent statistical meaning. Experimental results show potentials for real-life applications
Starting Page	291
Ending Page	296
File Size	223428
Page Count	6
File Format	PDF
ISBN	0769525171
ISSN	10637125
DOI	10.1109/CBMS.2006.17
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Medical services Biomedical equipment Fuzzy systems Supervised learning Design methodology Learning systems Computational intelligence Biomedical engineering Explosions Vector quantization
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Computer Science Applications

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