Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Myoungshic Jhun"'
Autor:
Jongkyeong Kang, Myoungshic Jhun
Publikováno v:
Journal of the Korean Data And Information Science Society. 31:501-511
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25e3843555f09163e80200772bbbc575
https://doi.org/10.7465/jkdi.2020.31.4.501
https://doi.org/10.7465/jkdi.2020.31.4.501
Autor:
Jongkyeong Kang, Myoungshic Jhun
Publikováno v:
Journal of the Korean Data And Information Science Society. 31:15-23
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e1184fe2f2f5719e968fd1c15436cdfc
https://doi.org/10.7465/jkdi.2020.31.1.15
https://doi.org/10.7465/jkdi.2020.31.1.15
Publikováno v:
Korean Journal of Applied Statistics. 30:733-745
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59a1b18bcadfec1cc6052099bba410f2
https://doi.org/10.5351/kjas.2017.30.5.733
https://doi.org/10.5351/kjas.2017.30.5.733
Autor:
Byun Taehyun, Myoungshic Jhun
Publikováno v:
The Korean Data Analysis Society. 19:1295-1302
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::212239234266ad3a89212377ba2bb98d
https://doi.org/10.37727/jkdas.2017.19.3.1295
https://doi.org/10.37727/jkdas.2017.19.3.1295
Autor:
Jae Lee1 jael@korea.ac.kr, Myoungshic Jhun1 jhun@korea.ac.kr, Jong Kim1 bbong95@naver.com, JungBok Lee2 jungboklee@korea.ac.kr
Publikováno v:
OR Spectrum. Apr2008, Vol. 30 Issue 2, p235-248. 14p. 3 Charts, 5 Graphs.
Autor:
Byoung Cheol Jung1 bcjung@ous.ac.kr, Myoungshic Jhun2 jhun@korea.ac.kr, Seuck Heun Song2 ssong@korea.ac.kr
Publikováno v:
Statistical Papers. Jan2007, Vol. 48 Issue 1, p47-62. 16p.
Publikováno v:
Korean Journal of Applied Statistics. 29:961-975
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02f83f05b1e1ae35db9bb035fa2eb61e
https://doi.org/10.5351/kjas.2016.29.5.961
https://doi.org/10.5351/kjas.2016.29.5.961
Publikováno v:
International Journal of Machine Learning and Cybernetics. 8:1211-1221
When input features are naturally grouped or generated by factors in a linear classification problem, it is more meaningful to identify important groups or factors rather than individual features. The F ∞-norm support vector machine (SVM) and the g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0585018f5598496f09c782aea448749
https://doi.org/10.1007/s13042-016-0494-2
https://doi.org/10.1007/s13042-016-0494-2
Publikováno v:
Communications in Statistics - Simulation and Computation. 46:2228-2240
The composite quantile regression (CQR) has been developed for the robust and efficient estimation of regression coefficients in a liner regression model. By employing the idea of the CQR, we propose a new regression method, called composite kernel q
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::71d4b147711c811eb1611b63cbcce9c0
https://doi.org/10.1080/03610918.2015.1039133
https://doi.org/10.1080/03610918.2015.1039133
Publikováno v:
Korean Journal of Applied Statistics. 28:485-494
Canonical correspondence analysis is an ordination method used to visualize the relationships among sites, species and environmental variables. However, projection results are fluctuations if the samples slightly change and consistent interpretation
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9713a4fc77cc4a272dd2bd618576fe0b
https://doi.org/10.5351/kjas.2015.28.3.485
https://doi.org/10.5351/kjas.2015.28.3.485