A modified generalized RBF model with EM-based learning algorithm for medical applications

Ma, Li Ya and Abdul Rahman, Abdul Wahab and Quek, Chai (2006) A modified generalized RBF model with EM-based learning algorithm for medical applications. In: Nineteenth IEEE International Symposium on Computer-Based Medical Systems, 22–23 June 2006, Salt Lake City, Utah.

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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 will 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 will 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.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Additional Information:	6145/38177
Uncontrolled Keywords:	RBF model, EM-based learning algorithm
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Prof Abdul Wahab Abdul Rahman
Date Deposited:	17 Sep 2014 15:03
Last Modified:	16 Dec 2020 23:47
URI:	http://irep.iium.edu.my/id/eprint/38177

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