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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2015.8
- 수록면
- 1,017 - 1,025 (9page)
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In mathematical statistics education, we can use mutual information as a tool for evaluating the degree of dependency between two random variables. The ordinary correlation coefficient provides information only on linear dependency, not on nonlinear relationship between two random variables if any. In this paper as a measure of the degree of dependency between random variables, we suggest the use of symmetric uncertainty and λ which are defined in terms of mutual information. They can be also considered as generalized correlation coefficients for both linear and non-linear dependence of random variables.
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참고문헌 (16)
참고문헌 신청
[학술지(정기간행물)] - Block, H. B. / 1974 / A continuous bivariate exponential extension / Journal of the American Statistical Association 69 : 1031 ~ 1037
[학술지(정기간행물)] - Chelikani, S. / 2003 / Support vector machine density estimator as a generalized Parzen windows estimator for mutual information based image registration / Lecture Notes in Computer Science 2879 : 854 ~ 861
[단행본] - Cover, T. M. / 1991 / Elements of information theory / Wiley
[보고서] - Darbellay, G. A. / 1998 / An adaptive histogram estimator for the estimator for the mutual information
[학술지(정기간행물)] - Darbellay, G. A. / 1999 / An estimator of the mutual information based on a criterion for independence / Computational Statistics and Data Analysis 32 : 1 ~ 17
[학술지(정기간행물)] - Chelikani, S. / 2003 / Support vector machine density estimator as a generalized Parzen windows estimator for mutual information based image registration / Lecture Notes in Computer Science 2879 : 854 ~ 861
[단행본] - Cover, T. M. / 1991 / Elements of information theory / Wiley
[보고서] - Darbellay, G. A. / 1998 / An adaptive histogram estimator for the estimator for the mutual information
[학술지(정기간행물)] - Darbellay, G. A. / 1999 / An estimator of the mutual information based on a criterion for independence / Computational Statistics and Data Analysis 32 : 1 ~ 17
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UCI(KEPA) : I410-ECN-0101-2018-041-001376870