Virtual Sampling with Data Construction Analysis
Chun‐Jung Huang, Hsiao‐Fan Wang, Shouyang Wang · IGI Global eBooks · 2009
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.