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

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 journal › Article › peer-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 language	English
Pages (from-to)	17514-17520
Number of pages	7
Journal	ARPN Journal of Engineering and Applied Sciences
Volume	10
Issue number	23
Publication status	Published - 2015
Externally published	Yes

Keywords

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

ASJC Scopus subject areas

General Engineering

Cite this

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

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.",

keywords = "And LOOCV, Genetic algorithm, Noise, Ridge regression, Small sample problem, Weighted kernel regression",

author = "Zuwairie Ibrahim and Shapiai, {Mohd Ibrahim} and Satiman, {Siti Nurzulaikha} and Mohamad, {Mohd Saberi} and Arshad, {Nurul Wahidah}",

year = "2015",

language = "English",

volume = "10",

pages = "17514--17520",

journal = "ARPN Journal of Engineering and Applied Sciences",

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AU - Ibrahim, Zuwairie

AU - Shapiai, Mohd Ibrahim

AU - Satiman, Siti Nurzulaikha

AU - Mohamad, Mohd Saberi

AU - Arshad, Nurul Wahidah

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - And LOOCV

KW - Genetic algorithm

KW - Noise

KW - Ridge regression

KW - Small sample problem

KW - Weighted kernel regression

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IS - 23

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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