What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Machine learning, Fuzzy logic and Cluster analysis.
Shitong Wang works mostly in the field of Artificial intelligence, limiting it down to topics relating to Algorithm and, in certain cases, Kernel density estimation, as a part of the same area of interest.
His Pattern recognition study integrates concerns from other disciplines, such as Discriminant function analysis and Task.
As part of one scientific family, Shitong Wang deals mainly with the area of Machine learning, narrowing it down to issues related to the Fuzzy classification, and often Fuzzy set operations and Neuro-fuzzy.
His Fuzzy logic research incorporates elements of Transfer of learning and Artificial neural network.
In Cluster analysis, Shitong Wang works on issues like Data mining, which are connected to Mixture model and Subspace clustering.
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?
His primary scientific interests are in Artificial intelligence, Pattern recognition, Cluster analysis, Fuzzy logic and Data mining.
His study in Machine learning extends to Artificial intelligence with its themes.
His Pattern recognition study frequently involves adjacent topics like Artificial neural network.
He interconnects Algorithm and Image segmentation in the investigation of issues within Cluster analysis.
The various areas that he examines in his Fuzzy logic study include Entropy and Interpretability.
Shitong Wang combines subjects such as Biclustering and Outlier with his study of Data mining.
He most often published in these fields:
- Artificial intelligence (76.03%)
- Pattern recognition (56.18%)
- Cluster analysis (30.71%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (76.03%)
- Pattern recognition (56.18%)
- Fuzzy logic (28.84%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial intelligence, Pattern recognition, Fuzzy logic, Cluster analysis and Support vector machine.
His study ties his expertise on Machine learning together with the subject of Artificial intelligence.
He has included themes like Image processing, Contextual image classification, Kernel and Robustness in his Pattern recognition study.
His Fuzzy logic research integrates issues from Artificial neural network, Interpretability, Adversarial system and Feature.
His research integrates issues of Fuzzy density, Entropy, Sample and Data mining in his study of Cluster analysis.
His work deals with themes such as Classifier, Margin, Data stream, Algorithm and Privacy preserving, which intersect with Support vector machine.
Between 2018 and 2021, his most popular works were:
- mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification (22 citations)
- Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns (20 citations)
- Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals (18 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Shitong Wang mainly focuses on Artificial intelligence, Cluster analysis, Pattern recognition, Data mining and Fuzzy logic.
His research in Artificial intelligence tackles topics such as Machine learning which are related to areas like Task analysis.
His Cluster analysis study combines topics in areas such as Space, Image and Information retrieval.
His study looks at the relationship between Pattern recognition and topics such as Kernel, which overlap with Estimator, Kernel density estimation and Kernel.
His study in Data mining is interdisciplinary in nature, drawing from both Linear programming, Lagrange multiplier and Contrast.
His Fuzzy logic study incorporates themes from Spectral clustering, Partition, Feature vector and Similarity matrix.
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