Virtual Sampling with Data Construction Analysis

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Author(s): Chun-Jung Huang (National Tsing Hua University, Taiwan, ROC), Hsiao-Fan Wang (Chinese Academy of Sciences, China)and Shouyang Wang (City University of Hong Kong, Hong Kong)
Copyright: 2009
Pages: 9
Source title: Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery
Source Author(s)/Editor(s): Hsiao-Fan Wang (National Tsing Hua University, ROC)
DOI: 10.4018/978-1-59904-982-3.ch018

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

One of the key problems in supervised learning is due to the insufficient size of the training data set. The natural way for an intelligent learning process to counter this problem and successfully generalize is to exploit prior information that may be available about the domain or that can be learned from prototypical examples. According to the concept of creating virtual samples, the intervalized kernel method of density estimation (IKDE) was proposed to improve the learning ability from a small data set. To demonstrate its theoretical validity, we provided a theorem based on Decomposition Theory. In addition, we proposed an alternative approach to achieving the better learning performance of IKDE.

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