Zobrazeno 1 - 10
of 733
pro vyhledávání: '"Lee, Ga Young"'
Publikováno v:
In BBA - Molecular Basis of Disease October 2024 1870(7)
Autor:
Lee, Hee Yul, Cho, Du Yong, Lee, Jin Hwan, Lee, Jihyun, Jeong, Jong Bin, Lee, Ji Ho, Lee, Ga Young, Jang, Mu Yeon, Cho, Kye Man
Publikováno v:
In Food Bioscience October 2024 61
Autor:
Lee, Hee Yul, Kim, Hyo Seon, Kim, Min Ju, Seo, Young Hye, Cho, Du Yong, Lee, Ji Ho, Lee, Ga Young, Jeong, Jong Bin, Jang, Mu Yeun, Lee, Jin Hwan, Lee, Jun, Cho, Kye Man
Publikováno v:
In Food Chemistry 15 December 2024 461
Data cleaning is the initial stage of any machine learning project and is one of the most critical processes in data analysis. It is a critical step in ensuring that the dataset is devoid of incorrect or erroneous data. It can be done manually with d
Externí odkaz:
http://arxiv.org/abs/2109.07127
Publikováno v:
In Nutrition Research July 2024 127:144-155
Publikováno v:
In The Journal of Nutritional Biochemistry February 2024 124
Autor:
Sinharoy, Arindam1 (AUTHOR) arindam.sinharoy004@gmail.com, Lee, Ga-Young1 (AUTHOR), Chung, Chong-Min1 (AUTHOR) cmchung@jj.ac.kr
Publikováno v:
International Journal of Molecular Sciences. May2024, Vol. 25 Issue 9, p4646. 20p.
Autor:
Sinharoy, Arindam1 (AUTHOR) arindam.sinharoy004@gmail.com, Lee, Ga-Young1 (AUTHOR), Chung, Chong-Min1 (AUTHOR) cmchung@jj.ac.kr
Publikováno v:
International Journal of Molecular Sciences. Apr2024, Vol. 25 Issue 7, p3960. 18p.
Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistencies in representing data values and entities. Learning over dirty databases may result in inaccurate
Externí odkaz:
http://arxiv.org/abs/2004.02308
Akademický článek
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