What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Algorithm
His scientific interests lie mostly in Artificial intelligence, Machine learning, Fuzzy logic, Data mining and Pattern recognition.
His study in Computer vision extends to Artificial intelligence with its themes.
His Association rule learning, Collaborative filtering, Recommender system and Multiview learning study in the realm of Machine learning connects with subjects such as Process.
His work on Fuzzy control system and Fuzzy set as part of general Fuzzy logic research is frequently linked to Approximation theory, thereby connecting diverse disciplines of science.
His research in Data mining intersects with topics in Cluster analysis and Time series.
In his work, Speech recognition is strongly intertwined with Pattern matching, which is a subfield of Pattern recognition.
His most cited work include:
- Collaborative Fuzzy Clustering From Multiple Weighted Views (160 citations)
- Generalized Fuzzy C-Means Clustering Algorithm With Improved Fuzzy Partitions (157 citations)
- Enhanced soft subspace clustering integrating within-cluster and between-cluster information (146 citations)
What are the main themes of his work throughout his whole career to date?
Fu-Lai Chung mainly focuses on Artificial intelligence, Pattern recognition, Data mining, Machine learning and Cluster analysis.
As a member of one scientific family, Fu-Lai Chung mostly works in the field of Artificial intelligence, focusing on Computer vision and, on occasion, Robustness.
His Data mining research is multidisciplinary, incorporating perspectives in Binary tree and Time series.
His biological study spans a wide range of topics, including Algorithm and Representation.
His Fuzzy classification study deals with Fuzzy set operations intersecting with Defuzzification.
When carried out as part of a general Fuzzy logic research project, his work on Fuzzy control system and Fuzzy set is frequently linked to work in Set, therefore connecting diverse disciplines of study.
He most often published in these fields:
- Artificial intelligence (59.37%)
- Pattern recognition (29.46%)
- Data mining (24.55%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (59.37%)
- Cluster analysis (19.64%)
- Pattern recognition (29.46%)
In recent papers he was focusing on the following fields of study:
Fu-Lai Chung mostly deals with Artificial intelligence, Cluster analysis, Pattern recognition, Theoretical computer science and Feature.
His Machine learning research extends to Artificial intelligence, which is thematically connected.
His Cluster analysis research incorporates elements of Probabilistic logic and Data mining.
His research investigates the link between Data mining and topics such as Contrast that cross with problems in Variety.
His work deals with themes such as Node, Embedding and Adversarial system, which intersect with Theoretical computer science.
He has researched Feature in several fields, including Task and Benchmark.
Between 2017 and 2021, his most popular works were:
- Deep Network Embedding for Graph Representation Learning in Signed Networks (23 citations)
- A Deep Bayesian Tensor-Based System for Video Recommendation (15 citations)
- Fast Exemplar-Based Clustering by Gravity Enrichment Between Data Objects (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Algorithm
His primary areas of investigation include Cluster analysis, Artificial intelligence, Transfer of learning, Algorithm and Bayesian probability.
His studies in Cluster analysis integrate themes in fields like Probabilistic logic, Data mining, Kernel and Kernel density estimation.
The various areas that Fu-Lai Chung examines in his Artificial intelligence study include Machine learning and Pattern recognition.
His Machine learning research incorporates themes from Knowledge transfer and Task.
His Transfer of learning research integrates issues from Cognitive neuroscience of visual object recognition, Kernel method, Linear model and Joint.
His Algorithm study combines topics in areas such as Huber loss, Computational intelligence and Support vector machine.
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