Zobrazeno 1 - 10
of 8 021
pro vyhledávání: '"Independent and identically distributed random variables"'
Publikováno v:
IEEE Transactions on Industrial Informatics. 19:561-569
The rapid progress of artificial intelligence expands its wide applicability in internet of things (IOT). Meanwhile, data insufficient and data source privacy are key supply chain challenges facing IOT especially in the healthcare industry. To addres
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a95247e8ad799c540c8af61f3a21a90a
https://doi.org/10.1109/tii.2021.3093715
https://doi.org/10.1109/tii.2021.3093715
Publikováno v:
IEEE Transactions on Cybernetics. 52:9951-9963
In this article, we consider the H∞ proportional-integral (PI) state estimation (SE) problem for discrete-time T-S fuzzy systems subject to transmission delays, external disturbances, and redundant channels. Multiple redundant communication channel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::80295eac335d113682f59e0be3f7f7f4
https://doi.org/10.1109/tcyb.2020.3036364
https://doi.org/10.1109/tcyb.2020.3036364
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:5009-5022
Ensemble learning, the machine learning paradigm where multiple algorithms are combined, has exhibited promising perfomance in a variety of tasks. The present work focuses on unsupervised ensemble classification. The term unsupervised refers to the e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9dd24358d667b4c635a4a285e570ec42
https://doi.org/10.1109/tkde.2020.3046645
https://doi.org/10.1109/tkde.2020.3046645
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:4228-4242
In most of the existing representation learning frameworks, the noise contaminating the data points is often assumed to be independent and identically distributed (i.i.d.), where the Gaussian distribution is often imposed. This assumption, though gre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1d1de282f99e2605c9a048bd7c04957
https://doi.org/10.1109/tnnls.2021.3056188
https://doi.org/10.1109/tnnls.2021.3056188
Publikováno v:
IEEE Transactions on Fuzzy Systems. 30:3166-3175
A fuzzy first order and second moment method (FFOSM) is proposed to efficiently estimate time-independent and time-dependent failure credibility in the presence of fuzzy uncertainty. For the time-independent linear performance function with independe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4b75551baa7fb940e2d72a20f0012d36
https://doi.org/10.1109/tfuzz.2021.3105718
https://doi.org/10.1109/tfuzz.2021.3105718
Publikováno v:
IEEE Transactions on Computers. 71:1655-1667
Federated learning (FL) has been widely recognized as a promising approach by enabling individual end-devices to cooperatively train a global model without exposing their own data. One of the key challenges in FL is the non-independent and identicall
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2fd98736cd87f228ad1a3e042ccfd1fa
https://doi.org/10.1109/tc.2021.3099723
https://doi.org/10.1109/tc.2021.3099723
Autor:
Zhe Jiang, Wenchong He
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:2912-2920
Semi-supervised learning aims to learn prediction models from both labeled and unlabeled samples. There has been extensive research in this area. Among existing work, generative mixture models with Expectation-Maximization (EM) is a popular method du
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90b9cf8e0ea3813855dae3a96ccd13c1
https://doi.org/10.1109/tkde.2020.3019038
https://doi.org/10.1109/tkde.2020.3019038
Autor:
Safari Mukeru
Publikováno v:
Michigan Mathematical Journal. 73
In 1979, Pisier proved remarkably that a sequence of independent and identically distributed standard Gaussian random variables determines, via random Fourier series, a homogeneous Banach algebra P strictly contained in C(T), the class of continuous
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ac30075d9f626bfd168763f59c720ba
https://doi.org/10.1307/mmj/20205914
https://doi.org/10.1307/mmj/20205914
Publikováno v:
IEEE Internet of Things Journal. 9:7773-7782
In this paper, we consider the problem of federated learning (FL) with training data that are not independent and identically distributed (non-IID) across the clients. To cope with data heterogeneity, Iterative Federated Clustering Algorithm (IFCA) h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::81b8794cd17115648f504ee42136164b
https://doi.org/10.1109/jiot.2021.3113927
https://doi.org/10.1109/jiot.2021.3113927
Publikováno v:
Neurocomputing. 485:134-154
Federated learning (FL) is a distributed machine learning paradigm that allows training models on decentralized data over large-scale edge/mobile devices without collecting raw data. However, existing methods are still far from efficient and stable u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::86f73b3b3276b75f793a3ff2ecdc0783
https://doi.org/10.1016/j.neucom.2021.08.146
https://doi.org/10.1016/j.neucom.2021.08.146