The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 29-430
Full Name: Hyontai Sug
Position: Associate Professor
Age: ON
Sex: Male
Address: Division of Computer and Information Engineering, Dongseo University, Busan, 617-716
Country: KOREA
Tel: 82-51-320-1733
Tel prefix:
Fax: 82-51-327-8955
E-mail address: hyontai@yahoo.com
Other E-mails: shtdaum@hanmail.net
Title of the Paper: The Relationship of Sample Size and Accuracy in Radial Basis Function Networks
Authors as they appear in the Paper: Hyontai Sug
Email addresses of all the authors: hyontai@yahoo.com
Number of paper pages: 10
Abstract: Even though radial basis function networks are known to have good prediction accuracy in several domains, it is not known to decide a proper sample size like other data mining algorithms, so the task of deciding proper sample sizes for the networks tends to be arbitrary. As the size of samples grows, the improvement in error rates becomes better slowly. But we cannot use larger and larger samples, because we have limited training examples, and there is some fluctuation in accuracy depending on the sample sizes. This paper suggests a progressive resampling technique to cope with the fluction of prediction accuracy values for better radial basis function networks. The suggestion is proved by experiments with promising results.
Keywords: neural networks, radial basis function networks, sampling
EXTENSION of the file: .pdf
Special (Invited) Session: An Experimental Decision of Samples for RBF Neural Networks
Organizer of the Session: 613-607
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