Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Jidong Yuan"'
Publikováno v:
IEEE Access, Vol 8, Pp 185032-185044 (2020)
Time series symbolization based on the Symbolic Fourier Approximation (SFA) and a sliding window mechanism can effectively improve classification performance. Hence, it has become a research hotspot of time series representation learning. However, th
Externí odkaz:
https://doaj.org/article/0950005c011e4deb8a1ef67d1e1da4c6
Publikováno v:
International Journal of Distributed Sensor Networks, Vol 12 (2016)
Data streams, which can be considered as one of the primary sources of what is called big data, arrive continuously with high speed. The biggest challenge in data streams mining is to deal with concept drifts, during which ensemble methods are widely
Externí odkaz:
https://doaj.org/article/312e55d16bcf4693b49bd413b0ec34e4
Publikováno v:
Intelligent Data Analysis. 27:653-674
The key problem of time series classification is the similarity measure between time series. In recent years, efficient and accurate similarity measurement methods of time series have attracted extensive attention from researchers. According to the d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8eccdae910952ab490eadd4e7349a27f
https://doi.org/10.3233/ida-216548
https://doi.org/10.3233/ida-216548
Publikováno v:
Information Sciences. 619:762-780
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc5100393ebf3870c99a65866faf01f5
https://doi.org/10.1016/j.ins.2022.11.040
https://doi.org/10.1016/j.ins.2022.11.040
Publikováno v:
Neural Processing Letters. 55:1833-1846
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b9c36cde8bc71225e68d0b2f6e442c23
https://doi.org/10.1007/s11063-022-10965-9
https://doi.org/10.1007/s11063-022-10965-9
Autor:
Wenqin Jiang, Hongxian Chu, Yiyao Liu, Bin Chen, Yongcai Feng, Jixuan Lyu, Jidong Yuan, Lixin Wang, Jialin Li, Weiguo Hou
Publikováno v:
Science of The Total Environment. 889:164208
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::569824691f553d5e0c8771606b6f68aa
https://doi.org/10.1016/j.scitotenv.2023.164208
https://doi.org/10.1016/j.scitotenv.2023.164208
Publikováno v:
Knowledge and Information Systems. 64:143-174
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::85a9dbe37eec10187b0735c570e207af
https://doi.org/10.1007/s10115-021-01630-z
https://doi.org/10.1007/s10115-021-01630-z
Publikováno v:
Neurocomputing. 425:23-36
High-dimensional sparse clustering with compositional data is of great practical importance, as exemplified by applications in high-throughput gene expression profiles analysis. In this paper, we develop a compositional clustering framework based on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bb2c6436a182df11b1f19104f85d8971
https://doi.org/10.1016/j.neucom.2020.10.105
https://doi.org/10.1016/j.neucom.2020.10.105
Publikováno v:
Computational Intelligence and Neuroscience
Computational Intelligence and Neuroscience, Vol 2020 (2020)
Computational Intelligence and Neuroscience, Vol 2020 (2020)
As a representation of discriminative features, the time series shapelet has recently received considerable research interest. However, most shapelet-based classification models evaluate the differential ability of the shapelet on the whole training
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::60bb824f94fade2c916efb81a31ae02a
https://doi.org/10.1155/2020/1978310
https://doi.org/10.1155/2020/1978310
Publikováno v:
Soft Computing. 25:2965-2980
Compositional data refer to a vector with parts that are positive and subject to a constant-sum constraint. Examples of compositional data in the real world include a vector with each entry representing the weight of a stock in an investment portfoli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::65b272aaaab5ffd0cefff5f45fcbd5fd
https://doi.org/10.1007/s00500-020-05355-z
https://doi.org/10.1007/s00500-020-05355-z