What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Data mining, Machine learning, Pattern recognition and Feature selection.
In his study, Pattern recognition is strongly linked to Computer vision, which falls under the umbrella field of Artificial intelligence.
He combines subjects such as Naive Bayes classifier and Cluster analysis with his study of Data mining.
His Naive Bayes classifier study combines topics from a wide range of disciplines, such as FSA-Red Algorithm, AdaBoost and PageRank.
His study in Machine learning is interdisciplinary in nature, drawing from both Class and Set.
His studies in Pattern recognition integrate themes in fields like Subspace topology and Divergence.
His most cited work include:
- Top 10 algorithms in data mining (3313 citations)
- Data mining with big data (1756 citations)
- General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (1012 citations)
What are the main themes of his work throughout his whole career to date?
Xindong Wu mainly investigates Artificial intelligence, Data mining, Machine learning, Pattern recognition and Algorithm.
His studies link Natural language processing with Artificial intelligence.
His research integrates issues of Set and Cluster analysis in his study of Data mining.
His Crowdsourcing research extends to Machine learning, which is thematically connected.
Much of his study explores Pattern recognition relationship to Subspace topology.
His biological study spans a wide range of topics, including Wildcard character and Pattern matching.
He most often published in these fields:
- Artificial intelligence (53.47%)
- Data mining (39.41%)
- Machine learning (29.34%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (53.47%)
- Machine learning (29.34%)
- Algorithm (13.72%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Machine learning, Algorithm, Theoretical computer science and Graph are his primary areas of study.
His Artificial intelligence research is multidisciplinary, incorporating elements of Task and Natural language processing.
His research on Machine learning often connects related areas such as Task analysis.
His Algorithm study incorporates themes from Pruning, Degree and Pattern matching.
Xindong Wu works mostly in the field of Graph, limiting it down to topics relating to Data science and, in certain cases, Domain knowledge, Human intelligence and Social media, as a part of the same area of interest.
His research investigates the link between Deep learning and topics such as Redundancy that cross with problems in Pattern recognition.
Between 2019 and 2021, his most popular works were:
- Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (18 citations)
- Deep Attributed Network Embedding by Preserving Structure and Attribute Information (12 citations)
- Causality-based Feature Selection: Methods and Evaluations (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Position, Algorithm, Machine learning and Feature selection.
The Benchmark research Xindong Wu does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Mechanism, therefore creating a link between diverse domains of science.
The concepts of his Benchmark study are interwoven with issues in Field, Word, Task, Java and Probabilistic latent semantic analysis.
His Algorithm research integrates issues from Property, Initialization, Stability and Constraint.
His Machine learning study combines topics in areas such as Generative grammar and Generative model.
Xindong Wu has included themes like Preprocessor, Bayesian network and Predictive modelling in his Feature selection study.
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