Zobrazeno 1 - 10
of 78
pro vyhledávání: '"Suyun Zhao"'
Publikováno v:
AI Open, Vol 3, Iss , Pp 200-207 (2022)
When faced with the issue of different feature distribution between training and test data, the test data may differ in style and background from the training data due to the collection sources or privacy protection. That is, the transfer generalizat
Externí odkaz:
https://doaj.org/article/e33ceca46b9740fd850c2d5676155926
Publikováno v:
Sensors, Vol 14, Iss 12, Pp 23905-23932 (2014)
Wireless sensor networks (WSNs) are indispensable building blocks for the Internet of Things (IoT). With the development of WSNs, privacy issues have drawn more attention. Existing work on the privacy-preserving range query mainly focuses on privacy
Externí odkaz:
https://doaj.org/article/071ae3138d2c43428f79902781deb4d1
Publikováno v:
IEEE Transactions on Cybernetics. 53:2200-2210
Fuzzy rough set (FRS) theory is generally used to measure the uncertainty of data. However, this theory cannot work well when the class density of a data distribution differs greatly. In this work, a relative distance measure is first proposed to fit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c11be9fa063c5b2f986d2b2558753b6
https://doi.org/10.1109/tcyb.2021.3112674
https://doi.org/10.1109/tcyb.2021.3112674
Publikováno v:
International Journal of Machine Learning and Cybernetics. 13:1603-1617
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f1c7aa5521d6e25596cf1f0afe6abda1
https://doi.org/10.1007/s13042-021-01470-x
https://doi.org/10.1007/s13042-021-01470-x
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. :1-13
Active learning(AL) has been successful based on the premise that labeled and unlabeled data come from the same class distribution. However, its performance undergoes a severe deterioration under class distribution mismatch, wherein the unlabeled dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d28d9256e674d319a6332e1c1eff34e5
https://doi.org/10.1109/tpami.2022.3188807
https://doi.org/10.1109/tpami.2022.3188807
Publikováno v:
IEEE Transactions on Fuzzy Systems. 29:3635-3649
Rule-based classifier, that extract a subset of induced rules to efficiently learn/mine while preserving the discernibility information, plays a crucial role in human-explainable artificial intelligence. However, in this era of big data, rule inducti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a4c1aba61cb94e6766d0e372dfd4dc7
https://doi.org/10.1109/tfuzz.2021.3101935
https://doi.org/10.1109/tfuzz.2021.3101935
Publikováno v:
International Journal of Approximate Reasoning. 139:130-142
Uncertainty measure is an important tool for data analysis. In practical applications, the collected data are subject to different probability distributions. This requires that the uncertainty measure has generalization performance. Fuzzy rough set (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2bc67a7c2a8700eba17e0d546b67e1a8
https://doi.org/10.1016/j.ijar.2021.09.014
https://doi.org/10.1016/j.ijar.2021.09.014
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
Partial label learning (PLL) is to learn a discriminative model under incomplete supervision, where each instance is annotated with a candidate label set. The basic principle of PLL is that the unknown correct label y of an instance x resides in its
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28b97adebb1d62a2164279ffa4444e49
https://doi.org/10.24963/ijcai.2022/456
https://doi.org/10.24963/ijcai.2022/456
Publikováno v:
Journal of Computer Science and Technology. 36:590-605
Partial label learning is a weakly supervised learning framework in which each instance is associated with multiple candidate labels, among which only one is the ground-truth label. This paper proposes a unified formulation that employs proper label
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f715510583ddc169e28dc43556d109b1
https://doi.org/10.1007/s11390-021-0992-x
https://doi.org/10.1007/s11390-021-0992-x
Publikováno v:
Frontiers of Computer Science. 16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31de09860548602d49e72b347f6887e5
https://doi.org/10.1007/s11704-021-0412-y
https://doi.org/10.1007/s11704-021-0412-y