Zobrazeno 1 - 10
of 185 111
pro vyhledávání: '"data stream"'
Autor:
Alice Mattoni, Diego Ceccobelli
Available Open Access digitally under CC-BY-ND licence This book pulls back the curtain on the link between activism, media and technology in the quiet times of politics when people are not protesting. Introducing the novel concept of the ‘data str
Autor:
Zhu, Xinchun1 (AUTHOR) zhuxc@yn.csg.cn, Wu, Yang1 (AUTHOR) wuy@yn.csg.cn, Zhao, Xu1 (AUTHOR) zhaoxu@yn.csg.cn, Yang, Yunchen1 (AUTHOR) yangyunchen@yn.csg.cn, Liu, Shuangquan1 (AUTHOR) liushuangquan@yn.csg.cn, Shi, Luyi2 (AUTHOR) 15997661995@163.com, Wu, Yelong3 (AUTHOR) liushuangquan@yn.csg.cn
Publikováno v:
Energies (19961073). Sep2024, Vol. 17 Issue 17, p4371. 24p.
The following work addresses the problem of frameworks for data stream processing that can be used to evaluate the solutions in an environment that resembles real-world applications. The definition of structured frameworks stems from a need to reliab
Externí odkaz:
http://arxiv.org/abs/2411.06799
Autor:
Przybyl, Bartosz, Stefanowski, Jerzy
Learning classifiers from imbalanced and concept drifting data streams is still a challenge. Most of the current proposals focus on taking into account changes in the global imbalance ratio only and ignore the local difficulty factors, such as the mi
Externí odkaz:
http://arxiv.org/abs/2410.03519
Tabular data is considered the last unconquered castle of deep learning, yet the task of data stream classification is stated to be an equally important and demanding research area. Due to the temporal constraints, it is assumed that deep learning me
Externí odkaz:
http://arxiv.org/abs/2407.10807
Autor:
Kumar, Jay
A text stream is an ordered sequence of text documents generated over time. A massive amount of such text data is generated by online social platforms every day. Designing an algorithm for such text streams to extract useful information is a challeng
Externí odkaz:
http://arxiv.org/abs/2409.00010
Autor:
Zyblewski, Paweł
Rapid technological advances are inherently linked to the increased amount of data, a substantial portion of which can be interpreted as data stream, capable of exhibiting the phenomenon of concept drift and having a high imbalance ratio. Consequentl
Externí odkaz:
http://arxiv.org/abs/2404.15836
Autor:
Chen, Yu-Hsi
In the realm of continual learning, the presence of noisy labels within data streams represents a notable obstacle to model reliability and fairness. We focus on the data stream scenario outlined in pertinent literature, characterized by fuzzy task b
Externí odkaz:
http://arxiv.org/abs/2404.04871