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pro vyhledávání: '"Lughofer, Edwin"'
Autor:
Ma'sum, Muhammad Anwar, Pratama, Mahardhika, Lughofer, Edwin, Liu, Lin, Habibullah, Kowalczyk, Ryszard
Existing approaches on continual learning call for a lot of samples in their training processes. Such approaches are impractical for many real-world problems having limited samples because of the overfitting problem. This paper proposes a few-shot co
Externí odkaz:
http://arxiv.org/abs/2306.14369
This paper proposes an assessor-guided learning strategy for continual learning where an assessor guides the learning process of a base learner by controlling the direction and pace of the learning process thus allowing an efficient learning of new e
Externí odkaz:
http://arxiv.org/abs/2303.11624
Autor:
Lughofer, Edwin
Multi-label classification has attracted much attention in the machine learning community to address the problem of assigning single samples to more than one class at the same time. We propose an evolving multi-label fuzzy classifier (EFC-ML) which i
Externí odkaz:
http://arxiv.org/abs/2203.15318
Unsupervised continual learning remains a relatively uncharted territory in the existing literature because the vast majority of existing works call for unlimited access of ground truth incurring expensive labelling cost. Another issue lies in the pr
Externí odkaz:
http://arxiv.org/abs/2106.14563
Publikováno v:
International Joint Conference on Neural Networks, 2021
The common practice of quality monitoring in industry relies on manual inspection well-known to be slow, error-prone and operator-dependent. This issue raises strong demand for automated real-time quality monitoring developed from data-driven approac
Externí odkaz:
http://arxiv.org/abs/2106.13955
Publikováno v:
Information Sciences, 2021
The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervise
Externí odkaz:
http://arxiv.org/abs/2107.02943
Publikováno v:
In Fuzzy Sets and Systems 30 August 2023 466
Transferring knowledge across many streaming processes remains an uncharted territory in the existing literature and features unique characteristics: no labelled instance of the target domain, covariate shift of source and target domain, different pe
Externí odkaz:
http://arxiv.org/abs/1910.03434