Zobrazeno 1 - 10
of 300
pro vyhledávání: '"Fu-Lai Chung"'
Publikováno v:
IEEE Access, Vol 7, Pp 103863-103875 (2019)
In bioinformatics, the vast of multi-label type of datasets, including clinical text, gene, and protein data, need to be categorized. Specifically, due to the redundant or irrelevant features in bioinformatics data, the performance of multi-label cla
Externí odkaz:
https://doaj.org/article/1dfdd7b5e5f64950ba5ada87ec63a6e9
Publikováno v:
Automatika, Vol 60, Iss 1, Pp 58-67 (2019)
We propose a novel two-party privacy-preserving classification solution called Collaborative Classification Mechanism for Privacy-preserving ( $ {\rm C}^{2}{\rm MP}^{2} $ ) over horizontally partitioned data that is inspired from the fact, that globa
Externí odkaz:
https://doaj.org/article/e99a0bd723104e04b4046d6040a5b07c
Publikováno v:
ACM Transactions on Spatial Algorithms and Systems. 9:1-25
Cities are very complex systems. Representing urban regions are essential for exploring, understanding, and predicting properties and features of cities. The enrichment of multi-modal urban big data has provided opportunities for researchers to enhan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c7a7aa2a21f649a1198b530c82b0be38
https://doi.org/10.1145/3571741
https://doi.org/10.1145/3571741
Publikováno v:
Information Sciences. 623:791-811
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2757dda023177c1c3fd14f305b6eecee
https://doi.org/10.1016/j.ins.2022.12.030
https://doi.org/10.1016/j.ins.2022.12.030
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-14
Graph anomaly detection (GAD) is a vital task since even a few anomalies can pose huge threats to benign users. Recent semi-supervised GAD methods, which can effectively leverage the available labels as prior knowledge, have achieved superior perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::37852e093152b789e6e2df96998e171a
https://doi.org/10.1109/tkde.2023.3271771
https://doi.org/10.1109/tkde.2023.3271771
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. :1-14
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::752148dc092a4033ce03fee2b02ee64e
https://doi.org/10.1109/tnnls.2023.3242049
https://doi.org/10.1109/tnnls.2023.3242049
Publikováno v:
IEEE Transactions on Fuzzy Systems. 30:4369-4383
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a4d1fb217e867a329b1ff9802223172
https://doi.org/10.1109/tfuzz.2022.3149395
https://doi.org/10.1109/tfuzz.2022.3149395
Publikováno v:
IEEE Transactions on Cybernetics. 52:6857-6871
While input or output-perturbation-based adversarial training techniques have been exploited to enhance the generalization capability of a variety of nonfuzzy and fuzzy classifiers by means of dynamic regularization, their performance may perhaps be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f005cce7d0bf67fc95f4ebf2c2e7468d
https://doi.org/10.1109/tcyb.2020.3034792
https://doi.org/10.1109/tcyb.2020.3034792
Publikováno v:
Information Sciences. 599:84-103
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::406329ce72cae45e60c5af4328229f0d
https://doi.org/10.1016/j.ins.2022.03.071
https://doi.org/10.1016/j.ins.2022.03.071
Publikováno v:
Information Fusion. 98:101845
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::086d6321860bec34be5ee3c87dd7dba6
https://doi.org/10.1016/j.inffus.2023.101845
https://doi.org/10.1016/j.inffus.2023.101845