Zobrazeno 1 - 10
of 9 135
pro vyhledávání: '"Active learning (machine learning)"'
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 34:186-200
Classification methods for streaming data are not new, but very few current frameworks address all three of the most common problems with these tasks: concept drift, noise, and the exorbitant costs associated with labeling the unlabeled instances in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::597667785749ed80d44f412eb667c9d2
https://doi.org/10.1109/tnnls.2021.3091681
https://doi.org/10.1109/tnnls.2021.3091681
Publikováno v:
IEEE Transactions on Circuits and Systems for Video Technology. 32:8101-8115
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::704e2cb4db05402bb4762cb317e0530f
https://doi.org/10.1109/tcsvt.2021.3079991
https://doi.org/10.1109/tcsvt.2021.3079991
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:5251-5262
Given a question and a set of candidate answers, answer selection is the task of identifying the best answer, which can be viewed as a kind of learning-to-rank tasks. Learning to rank arises in many information retrieval applications, where deep lear
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc72916c8fec090bf08391a05acb0b9a
https://doi.org/10.1109/tkde.2021.3056894
https://doi.org/10.1109/tkde.2021.3056894
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:4921-4932
Active learning is an effective approach for tasks with limited labeled data. It samples a small set of data to annotate actively and is widely applied in various AI tasks. It uses an iterative process, during which we utilize the current trained mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6bbf68ead0e760d61d38849368afce47
https://doi.org/10.1109/tkde.2020.3045816
https://doi.org/10.1109/tkde.2020.3045816
Publikováno v:
IEEE Transactions on Network Science and Engineering. 9:3282-3291
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::150e5d798441164326c1917d15510089
https://doi.org/10.1109/tnse.2021.3100322
https://doi.org/10.1109/tnse.2021.3100322
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:3971-3983
Applications challenged by the joint problem of concept drift and class imbalance are attracting increasing research interest. This paper proposes a novel Reinforcement Online Active Learning Ensemble for Drifting Imbalanced data stream (ROALE-DI). T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::320f166c229c6fc335f8964cc27d4913
https://doi.org/10.1109/tkde.2020.3026196
https://doi.org/10.1109/tkde.2020.3026196
Publikováno v:
IEEE Transactions on Affective Computing. 13:1155-1167
In this article, we introduce a next-generation annotation tool called NOVA for emotional behaviour analysis, which implements a workflow that interactively incorporates the ‘human in the loop’. A main aspect of NOVA is the possibility of applyin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0e9bbc8bfaf6a7318464ff77fac426c3
https://doi.org/10.1109/taffc.2020.3043603
https://doi.org/10.1109/taffc.2020.3043603
Autor:
Song-Nam Hong, Jeongmin Chae
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 33:2980-2994
Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently al
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa6f5a3ad2f614383db90102e292362d
https://doi.org/10.1109/tnnls.2020.3047953
https://doi.org/10.1109/tnnls.2020.3047953
Autor:
Jens Meiler, John A. Capra, Walter J. Chazin, Alexandra M Blee, Zachary D. Nagel, Bian Li, T J Pecen
Publikováno v:
Cancer Res
For precision medicine to reach its full potential for treatment of cancer and other diseases, protein variant effect prediction tools are needed to characterize variants of unknown significance (VUS) in a patient's genome with respect to their likel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28ac8ed850da230af9f0269ea665cd26
https://doi.org/10.1158/0008-5472.can-21-3798
https://doi.org/10.1158/0008-5472.can-21-3798
Publikováno v:
IEEE Transactions on Systems, Man, and Cybernetics: Systems. 52:3079-3091
Abundant data with limited labeling are a widespread bottleneck in multilabel learning. Active learning (AL) is an effective solution to gradually enhance model robustness, however how to effectively extend instance selection criteria to multilabel c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0982cdb1c37ce4e09edd75e7724bb842
https://doi.org/10.1109/tsmc.2021.3062714
https://doi.org/10.1109/tsmc.2021.3062714