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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.1
- 수록면
- 227 - 235 (9page)
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When the spatial information of each location is given specifically as coordinates it is popular to use the geographically weighted regression to incorporate the spatial information by assuming that the regression parameters vary spatially across locations. In this paper, we relax the linearity assumption of geographically weighted regression and propose a geographically weighted least squares-support vector machine for estimating geographically weighted mean by using the basic concept of kernel machines. Generalized cross validation function is induced for the model selection. Numerical studies with real datasets have been conducted to compare the performance of proposed method with other methods for predicting geographically weighted mean.
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참고문헌 (19)
참고문헌 신청
[단행본] - Anselin, L. / 1992 / Spatial econometrics: Method and models / Kluwer Academic Publishers
[학술지(정기간행물)] - Brunsdon, C. / 1999 / Some notes on Parametric significance tests for geographically weighted regression / Journal of Regional Science 39 : 497 ~ 524
[단행본] - Fotheringham, A. S. / 2002 / Geographically weighted regressio / John Wiley and Sons
[학술지(정기간행물)] - Fotheringham, A. S. / 1996 / The geography of parameter space : An investigation of spatial non-stationarity / International Journal of Geographical Information Science 10 : 605 ~ 627
[학술지(정기간행물)] - 황창하 / 2016 / Deep LS-SVM for regression / 한국데이터정보과학회지 27 (3) : 827 ~ 833
[학술지(정기간행물)] - Brunsdon, C. / 1999 / Some notes on Parametric significance tests for geographically weighted regression / Journal of Regional Science 39 : 497 ~ 524
[단행본] - Fotheringham, A. S. / 2002 / Geographically weighted regressio / John Wiley and Sons
[학술지(정기간행물)] - Fotheringham, A. S. / 1996 / The geography of parameter space : An investigation of spatial non-stationarity / International Journal of Geographical Information Science 10 : 605 ~ 627
[학술지(정기간행물)] - 황창하 / 2016 / Deep LS-SVM for regression / 한국데이터정보과학회지 27 (3) : 827 ~ 833
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UCI(KEPA) : I410-ECN-0101-2018-041-001379119