2023 - Research.com Computer Science in China Leader Award
2012 - Fellow of the American Association for the Advancement of Science (AAAS)
2011 - IEEE Fellow For contributions to artificial intelligence applications in power systems
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.
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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Top 10 algorithms in data mining
Xindong Wu;Vipin Kumar;J. Ross Quinlan;Joydeep Ghosh.
Knowledge and Information Systems (2007)
Data mining with big data
Xindong Wu;Xingquan Zhu;Gong-Qing Wu;Wei Ding.
IEEE Transactions on Knowledge and Data Engineering (2014)
Object Detection With Deep Learning: A Review
Zhong-Qiu Zhao;Peng Zheng;Shou-Tao Xu;Xindong Wu.
IEEE Transactions on Neural Networks (2019)
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
Dacheng Tao;Xuelong Li;Xindong Wu;S.J. Maybank.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)
Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval
Dacheng Tao;Xiaoou Tang;Xuelong Li;Xindong Wu.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)
Class noise vs. attribute noise: a quantitative study of their impacts
Xingquan Zhu;Xindong Wu.
Artificial Intelligence Review (2003)
The Top Ten Algorithms in Data Mining
Xindong Wu;Vipin Kumar.
10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH
Qiang Yang;Xindong Wu.
International Journal of Information Technology and Decision Making (2006)
Efficient mining of both positive and negative association rules
Xindong Wu;Chengqi Zhang;Shichao Zhang.
ACM Transactions on Information Systems (2004)
Geometric Mean for Subspace Selection
Dacheng Tao;Xuelong Li;Xindong Wu;S.J. Maybank.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: