Fu-Lai Chung
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Explore the profile of Fu-Lai Chung including associated specialties, affiliations and a list of published articles.
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Articles
27
Citations
149
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0
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Recent Articles
1.
Zhou S, Huang X, Liu N, Zhang W, Zhang Y, Chung F
Neural Netw
. 2024 Jul;
179:106551.
PMID: 39068675
Automatic electrocardiogram (ECG) classification provides valuable auxiliary information for assisting disease diagnosis and has received much attention in research. The success of existing classification models relies on fitting the labeled...
2.
Bian Z, Zhang J, Chung F, Wang S
IEEE Trans Neural Netw Learn Syst
. 2023 Apr;
35(8):10461-10474.
PMID: 37022881
Motivated by both the commonly used "from wholly coarse to locally fine" cognitive behavior and the recent finding that simple yet interpretable linear regression model should be a basic component...
3.
Qin B, Chung F, Wang S
IEEE Trans Cybern
. 2020 Dec;
52(7):6857-6871.
PMID: 33284765
While input or output-perturbation-based adversarial training techniques have been exploited to enhance the generalization capability of a variety of nonfuzzy and fuzzy classifiers by means of dynamic regularization, their performance...
4.
Shen X, Dai Q, Mao S, Chung F, Choi K
IEEE Trans Neural Netw Learn Syst
. 2020 Jun;
32(5):1935-1948.
PMID: 32497008
Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing network embedding algorithms are mostly developed for a...
5.
Jiang Z, Chung F, Wang S
IEEE Trans Neural Syst Rehabil Eng
. 2019 Mar;
27(4):630-642.
PMID: 30872235
Electroencephalogram (EEG) signal recognition based on machine learning models is becoming more and more attractive in epilepsy detection. For multiclass epileptic EEG signal recognition tasks including the detection of epileptic...
6.
Shen X, Chung F
IEEE Trans Cybern
. 2018 Oct;
50(4):1556-1568.
PMID: 30307885
Network embedding has attracted an increasing attention over the past few years. As an effective approach to solve graph mining problems, network embedding aims to learn a low-dimensional feature vector...
7.
Jiang Y, Wu D, Deng Z, Qian P, Wang J, Wang G, et al.
IEEE Trans Neural Syst Rehabil Eng
. 2017 Sep;
25(12):2270-2284.
PMID: 28880184
Recognition of epileptic seizures from offline EEG signals is very important in clinical diagnosis of epilepsy. Compared with manual labeling of EEG signals by doctors, machine learning approaches can be...
8.
Lu W, Chung F, Lai K, Zhang L
Neural Netw
. 2017 Jul;
93:256-266.
PMID: 28715693
Guessing what user may like is now a typical interface for video recommendation. Nowadays, the highly popular user generated content sites provide various sources of information such as tags for...
9.
Huang C, Chung F, Wang S
Neural Netw
. 2016 Jan;
75:110-25.
PMID: 26773824
In this paper, a novel L2-SVM based classifier Multi-view L2-SVM is proposed to address multi-view classification tasks. The proposed Multi-view L2-SVM classifier does not have any bias in its objective...
10.
Dong A, Chung F, Deng Z, Wang S
IEEE Trans Cybern
. 2015 Nov;
46(12):2924-2937.
PMID: 26571545
Many traditional semi-supervised learning algorithms not only train on the labeled samples but also incorporate the unlabeled samples in the training sets through an automated labeling process such as manifold...