Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Yi-Hung Kung"'
Autor:
Yu-Shan Shih, Yi-Hung Kung
Publikováno v:
Machine Learning and Knowledge Extraction, Vol 5, Iss 2, Pp 448-459 (2023)
In this article, we propose a tree-structured method for either complete or partial rank data that incorporates covariate information into the analysis. We use conditional independence tests based on hierarchical log-linear models for three-way conti
Externí odkaz:
https://doaj.org/article/865d0edc98854c589f7d6febe4396770
Publikováno v:
Entropy, Vol 25, Iss 9, p 1326 (2023)
This study introduces the Spacetimeformer model, a novel approach for predicting stock prices, leveraging the Transformer architecture with a time–space mechanism to capture both spatial and temporal interactions among stocks. Traditional Long–Sh
Externí odkaz:
https://doaj.org/article/0fd5c2c2bdac4d0eb31713d310890dfe
Autor:
Yi-Hung Kung, 龔一鴻
90
This paper concerns the exploitative competition of two species for three complementary resources in a chemostat. The predicted biological conditions which should give rise to each of the possible competitive outcomes are presented in detail
This paper concerns the exploitative competition of two species for three complementary resources in a chemostat. The predicted biological conditions which should give rise to each of the possible competitive outcomes are presented in detail
Externí odkaz:
http://ndltd.ncl.edu.tw/handle/75892888068038084686
Autor:
Hsiao-Hui Tsou, Chun-Hong Chen, Pei-Sheng Lin, Tzai-Hung Wen, Yi-Hung Kung, Hsiang-Yu Yuan, Li-Wei Chen
Publikováno v:
International Journal of Biometeorology. 63:259-268
Dengue is one of the most rapidly spreading mosquito-borne viral diseases in the world. An increase in the incidence of dengue is commonly thought to be a consequence of variability of weather conditions. Taiwan, which straddles the Tropic of Cancer,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e2cb7342219fc53223d0972d4ff6bf7
https://doi.org/10.1007/s00484-018-01659-w
https://doi.org/10.1007/s00484-018-01659-w
Publikováno v:
Statistics & Probability Letters. 94:248-256
In the study of complex organisms, clarifying the association between the evolution of coding genes and the measures of functional variables is of fundamental importance. However, traditional analysis of the evolutionary rate is either built on the a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2f0410546d0da08a4425a0dec818e13e
https://doi.org/10.1016/j.spl.2014.07.031
https://doi.org/10.1016/j.spl.2014.07.031
Publikováno v:
Statistics & Probability Letters. 82:1786-1791
A k -nearest neighbor method, which has been widely applied in machine learning, is a useful tool to obtain statistical inference for an underlying distribution of multi-dimensional data. However, the knowledge on choosing an optimal order for the k
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::20a2ef695cd01c53df2145888f769be5
https://doi.org/10.1016/j.spl.2012.05.017
https://doi.org/10.1016/j.spl.2012.05.017
Publikováno v:
Computational Statistics & Data Analysis. 56:2830-2836
A variable selection method for constructing decision trees with rank data is proposed. It utilizes conditional independence tests based on loglinear models for contingency tables. Compared with other selection methods, our method is computationally
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::35a7a19f60e39017bc17dc85093867cb
https://doi.org/10.1016/j.csda.2012.03.004
https://doi.org/10.1016/j.csda.2012.03.004
Publikováno v:
Biometrics. 72(4)
Summary In applying scan statistics for public health research, it would be valuable to develop a detection method for multiple clusters that accommodates spatial correlation and covariate effects in an integrated model. In this article, we connect t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::211d272298ba9c7b52035300f855fba3
https://pubmed.ncbi.nlm.nih.gov/26953633
https://pubmed.ncbi.nlm.nih.gov/26953633