Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Xiaoyuan, Su"'
Autor:
Li, Qi, Wenge, Tang, Ju, Wang, Yu, Xiong, Yi, Yuan, Baisong, Li, Lin, Yang, Tingting, Li, Lianjian, Yang, Xiaoyuan, Su, Qin, Li, Lijie, Zhang
Publikováno v:
China CDC weekly. 4(46)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron subvariant has a stronger transmission capacity and faster transmission speed than the previous strain.The first coronavirus disease 2019 (COVID-19) case infected with the SARS-CoV-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::d1466a0c50cfe7271831fe24366fb342
https://pubmed.ncbi.nlm.nih.gov/36483192
https://pubmed.ncbi.nlm.nih.gov/36483192
Publikováno v:
Soft Materials. 11:321-325
The syntheses of novel triphenylene-ferrocene dyads linked by long or short chain containing conjugated structures were reported in good yields. Their electrochemical properties and mesophase behaviors were investigated by cyclic voltammetry, differe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::08205edc14a3fbc90c188037c9747dd2
https://doi.org/10.1080/1539445x.2012.654584
https://doi.org/10.1080/1539445x.2012.654584
Autor:
Taghi M. Khoshgoftaar, Xiaoyuan Su
Publikováno v:
Advances in Artificial Intelligence. 2009:1-19
As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0119ce3901d975b8b55dd4236c4fb17
https://doi.org/10.1155/2009/421425
https://doi.org/10.1155/2009/421425
Autor:
Taghi M. Khoshgoftaar, Xiaoyuan Su
Publikováno v:
International Journal on Artificial Intelligence Tools. 17:71-85
As one of the most successful recommender systems, collaborative filtering (CF) algorithms are required to deal with high sparsity and high requirement of scalability amongst other challenges. Bayesian networks (BNs), one of the most frequently used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d5881c92d453a38cfedb05469e994e0b
https://doi.org/10.1142/s0218213008003789
https://doi.org/10.1142/s0218213008003789
Autor:
Xiaoyuan Su
Publikováno v:
2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions).
As one of the most successful approaches to building recommender systems, collaborative filtering (CF) uses the known preferences of a group of users to make recommendations or predictions of the unknown preferences for other users. In this paper, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ca10937262dc0988e918adfbdafdf66e
https://doi.org/10.1109/icrito.2015.7359203
https://doi.org/10.1109/icrito.2015.7359203
Publikováno v:
Machine Learning. 59:297-322
Bayesian belief nets (BNs) are often used for classification tasks--typically to return the most likely class label for each specified instance. Many BN-learners, however, attempt to find the BN that maximizes a different objective function--viz., li
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ee680c8926a3aedba7b22676eb2606a8
https://doi.org/10.1007/s10994-005-0469-0
https://doi.org/10.1007/s10994-005-0469-0
Publikováno v:
Journal of colloid and interface science. 369(1)
In this study, rice husk, an abundant agricultural byproduct, was utilized as an alternative silica source for the synthesis of MCM-22. The zeolite with high crystalline was synthesized using a three-stage varying-temperature hydrothermal method. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a13fdcf3f60279407b3853a58734f827
https://pubmed.ncbi.nlm.nih.gov/22226498
https://pubmed.ncbi.nlm.nih.gov/22226498
Publikováno v:
Advances in Knowledge Discovery and Data Mining ISBN: 9783642208409
PAKDD (1)
PAKDD (1)
Many learning algorithms perform poorly when the training data are incomplete. One standard approach involves first imputing the missing values, then giving the completed data to the learning algorithm. However, this is especially problematic when th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::067d7287fae46844c1e9447cbc995be4
https://doi.org/10.1007/978-3-642-20841-6_11
https://doi.org/10.1007/978-3-642-20841-6_11
Publikováno v:
Web Intelligence
Collaborative filtering (CF) is one of the most effective types of recommender systems. As data sparsity remains a significant challenge for CF, we consider basing predictions on imputed data, and find this often improves performance on very sparse r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec029c6aa45bf249f947152491906c1c
https://doi.org/10.1109/wiiat.2008.99
https://doi.org/10.1109/wiiat.2008.99
Publikováno v:
ICTAI (1)
It is difficult to learn good classifiers when training data is missing attribute values. Conventional techniques for dealing with such omissions, such as mean imputation, generally do not significantly improve the performance of the resulting classi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3af0dfe6917f4b558797d075b4e6ea78
https://doi.org/10.1109/ictai.2008.60
https://doi.org/10.1109/ictai.2008.60