Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Jiaxing Pi"'
Publikováno v:
European Journal of Operational Research. 290:844-856
Clustering techniques are powerful tools commonly used in statistical learning and data analytics. Most of the past research formulates clustering tasks as a non-convex problem, where a global optimum often cannot be found. Recent studies show that h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f807012e857a0a5e6f856cbfadad97a
https://doi.org/10.1016/j.ejor.2020.09.010
https://doi.org/10.1016/j.ejor.2020.09.010
Autor:
Vish Krishnan, Adam Hahn, K. Kaur, Sindhu Suresh, Anurag K. Srivastava, Yue Zhang, Jiaxing Pi
Publikováno v:
IEEE Transactions on Smart Grid. 11:931-941
With the significant increase in integration of renewable energy generation into the electric grid, market-based transactive exchanges between energy producers and prosumers will become more common. Transactive energy systems (TESs) employ economic a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b1003bca4bd018cf8cc86b77a0a1c1ef
https://doi.org/10.1109/tsg.2019.2928168
https://doi.org/10.1109/tsg.2019.2928168
Publikováno v:
Proceedings of the VLDB Endowment. 12:1195-1207
Keys for graphs incorporate the topology and value constraints needed to uniquely identify entities in a graph. They have been studied to support object identification, knowledge fusion, and social network reconciliation. Existing key constraints ide
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e284950ecd37b909a3be02a497770b1b
https://doi.org/10.14778/3339490.3339501
https://doi.org/10.14778/3339490.3339501
Publikováno v:
Journal of Data and Information Quality. 11:1-27
This article presents a new framework that incorporates graph patterns to support fact checking in knowledge graphs. Our method discovers discriminant graph patterns to construct classifiers for fact prediction. First, we propose a class of graph fac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc09c135da6f615b054cfa7957c8ef15
https://doi.org/10.1145/3286488
https://doi.org/10.1145/3286488
Publikováno v:
ICDE
This paper studies a class of neighborhood con-straints to characterize and repair erroneous entity information in multi-relational graph data. (1) We propose a class of constraints called star functional dependencies (StarFDs). Unlike conventional i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c39e0de2de983075473a9bf13f7d012c
https://doi.org/10.1109/icde48307.2020.00027
https://doi.org/10.1109/icde48307.2020.00027
Publikováno v:
Applied Soft Computing. 71:894-904
Human learning Optimization (HLO) is an emergent promising meta-heuristic algorithm which uses the random learning operator, the individual learning operator, and the social learning operator to search out the optimal solution of problems based on a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9f38c71c52ae6ab6c855131af9450d3
https://doi.org/10.1016/j.asoc.2018.07.051
https://doi.org/10.1016/j.asoc.2018.07.051
Publikováno v:
Journal of Global Optimization. 67:283-323
Human Learning Optimization is a simple but efficient meta-heuristic algorithm in which three learning operators, i.e. the random learning operator, the individual learning operator, and the social learning operator, are developed to efficiently sear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::385eb279327ffdeecf13ded4ffa55899
https://doi.org/10.1007/s10898-016-0444-2
https://doi.org/10.1007/s10898-016-0444-2
Publikováno v:
2016 32nd Southern Biomedical Engineering Conference (SBEC).
Raman Spectroscopy provides a non-invasive approach to study cells and tissues, and its ability to provide biochemical composition information of samples shows great importance for the research, diagnosis and treatment of cancer. However, conventiona
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::aed057ec5b3d0a3c06d57b331cd8f17a
https://doi.org/10.1109/sbec.2016.27
https://doi.org/10.1109/sbec.2016.27