Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Bojun Yan"'
Publikováno v:
Journal of Agricultural and Food Chemistry. 67:8085-8095
Herbicide resistance identification is essential for effective chemical weed control. In this study, we quantified the differences in growth response between penoxsulam resistant (R) and sensitive (S) Echinochloa crus-galli populations, explored the
Autor:
Yueqiang Ma, Tiecheng Feng, Chenlu Li, Bojun Yan, Kun Yang, Shunzheng Zhao, Xiaolong Tang, Xiaoxu Cui, Xi Yang, Honghong Yi
Publikováno v:
Journal of Chemical Technology & Biotechnology. 93:720-729
Publikováno v:
Journal of agricultural and food chemistry. 67(29)
Herbicide resistance identification is essential for effective chemical weed control. In this study, we quantified the differences in growth response between penoxsulam resistant (R) and sensitive (S)
Publikováno v:
Pesticide biochemistry and physiology. 158
Cytochrome P450s (P450s) confer resistance against herbicides, and this is increasingly becoming a concern for weed control. As a widespread Gramineae weed in paddy fields, Echinocloa glabrescens has become resistant to the acetolactate synthase (ALS
Publikováno v:
Heliyon, Vol 10, Iss 3, Pp e25141- (2024)
Despite international regulatory efforts, the marine areas beyond national jurisdiction continue to be subject to increasing levels of environmental stress and degradation from international shipping activities. Specifically, the absence of effective
Externí odkaz:
https://doaj.org/article/8e0121deacc04afcb2408bb8ca82c52c
Publikováno v:
Knowledge and Information Systems. 28:99-116
A critical problem related to kernel-based methods is how to select optimal kernels. A kernel function must conform to the learning target in order to obtain meaningful results. While solutions to the problem of estimating optimal kernel functions an
Autor:
Bojun Yan, Dimitrios Gunopulos, Sheng Ma, Carlotta Domeniconi, Dimitris Papadopoulos, Muna Al-Razgan
Publikováno v:
Data Mining and Knowledge Discovery. 14:63-97
Clustering suffers from the curse of dimensionality, and similarity functions that use all input features with equal relevance may not be effective. We introduce an algorithm that discovers clusters in subspaces spanned by different combinations of d
Autor:
Bojun Yan
Publikováno v:
Encyclopedia of Data Warehousing and Mining
As a recent emerging technique, semi-supervised clustering has attracted significant research interest. Compared to traditional clustering algorithms, which only use unlabeled data, semi-supervised clustering employs both unlabeled and supervised dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e0f9aae5b34280b1786a3d30de6db5f1
https://doi.org/10.4018/978-1-60566-010-3.ch177
https://doi.org/10.4018/978-1-60566-010-3.ch177
Autor:
Carlotta Domeniconi, Bojun Yan
Publikováno v:
SMC
Semi-supervised clustering makes use of a small amount of supervised data to aid unsupervised learning. The method used to obtain the supervised information, and the way such information is integrated within the learning algorithm can greatly affect