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Training Sample and Feature Selection Methods for Pseudo Sample Neural Networks

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2013, 18(4), pp.19-26
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

Gyeongyong Heo 1 Park Choong Shik 2 이창우 3

1동의대학교
2영동대학교
3국립산림과학원 남부산림연구소

Accredited

ABSTRACT

Pseudo sample neural network (PSNN) is a variant of traditional neural network using pseudo samples to mitigate the local-optima-convergence problem when the size of training samples is small. PSNN can take advantage of the smoothed solution space through the use of pseudo samples. PSNN has a focus on the quantity problem in training, whereas, methods stressing the quality of training samples is presented in this paper to improve further the performance of PSNN. It is evident that typical samples and highly correlated features help in training. In this paper,therefore, kernel density estimation is used to select typical samples and correlation factor is introduced to select features, which can improve the performance of PSNN. Debris flow data set is used to demonstrate the usefulness of the proposed methods.

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