A complete investigation of using weighted kernel regression for the case of small sample problem with noise

Zuwairie Ibrahim, Mohd Ibrahim Shapiai, Siti Nurzulaikha Satiman, Mohd Saberi Mohamad, Nurul Wahidah Arshad

Research output: Contribution to journalArticlepeer-review

Abstract

Weighted kernel regression (WKR) is a kernel-based regression approach for small sample problems. Previously, for the case of small sample problems with noise, we have done preliminary studies which investigated different learning techniques and different learning functions, separately. In this paper, a complete investigation of using WKR for the case of noisy and small training samples is presented. Analysis and discussion are provided in detail.

Original languageEnglish
Pages (from-to)17514-17520
Number of pages7
JournalARPN Journal of Engineering and Applied Sciences
Volume10
Issue number23
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • And LOOCV
  • Genetic algorithm
  • Noise
  • Ridge regression
  • Small sample problem
  • Weighted kernel regression

ASJC Scopus subject areas

  • General Engineering

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