2022 - Research.com Computer Science in China Leader Award
2019 - Edward J. McCluskey Technical Achievement Award, IEEE Computer Society For contributions to machine learning and data mining.
2017 - Member of Academia Europaea
2016 - Fellow of the American Association for the Advancement of Science (AAAS)
2016 - ACM Fellow For contributions to machine learning and data mining.
2016 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to ensemble methods and learning from multi-labeled and partially-labeled data.
2013 - IEEE Fellow For contributions to learning systems in data mining and pattern recognition
2013 - ACM Distinguished Member
2011 - ACM Senior Member
His scientific interests lie mostly in Artificial intelligence, Machine learning, Pattern recognition, Training set and Ensemble learning. His Artificial intelligence study frequently links to other fields, such as Computer vision. Machine learning is closely attributed to Data mining in his research.
His Data mining research incorporates elements of Random forest, Support vector machine and Sample. The Pattern recognition study combines topics in areas such as Image, Set and Cluster analysis. His Cluster analysis research focuses on Algorithm and how it connects with Statistical classification.
Zhi-Hua Zhou mainly focuses on Artificial intelligence, Machine learning, Pattern recognition, Mathematical optimization and Data mining. His Artificial intelligence research focuses on Semi-supervised learning, Ensemble learning, Artificial neural network, Training set and Supervised learning. His biological study spans a wide range of topics, including Stability and Active learning.
Machine learning connects with themes related to Task in his study. Within one scientific family, he focuses on topics pertaining to Facial recognition system under Pattern recognition, and may sometimes address concerns connected to Pattern recognition. His research on Data mining frequently links to adjacent areas such as Cluster analysis.
His primary areas of study are Artificial intelligence, Machine learning, Mathematical optimization, Deep learning and Regret. In most of his Artificial intelligence studies, his work intersects topics such as Pattern recognition. His research investigates the connection between Machine learning and topics such as Key that intersect with problems in Representation.
His research integrates issues of Margin, Margin distribution, Constraint and Selection in his study of Mathematical optimization. His Deep learning research focuses on subjects like Artificial neural network, which are linked to Leverage. His Regret research focuses on Convex optimization and how it relates to Sequence, Convex function, Measure and Convexity.
Zhi-Hua Zhou mostly deals with Artificial intelligence, Machine learning, Regret, Deep learning and Mathematical optimization. Zhi-Hua Zhou has researched Artificial intelligence in several fields, including Task analysis and Pattern recognition. His Machine learning research is multidisciplinary, incorporating elements of Data modeling, Representation and Task.
His Regret research incorporates themes from Data mining, Concept drift, Reusability, Convex optimization and Upper and lower bounds. His research investigates the link between Deep learning and topics such as Artificial neural network that cross with problems in Scripting language and Human intelligence. His work is dedicated to discovering how Training set, Supervised learning are connected with Noise and other disciplines.
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)
ML-KNN: A lazy learning approach to multi-label learning
Min-Ling Zhang;Zhi-Hua Zhou.
Pattern Recognition (2007)
Ensembling neural networks: many could be better than all
Zhi-Hua Zhou;Jianxin Wu;Wei Tang.
Artificial Intelligence (2002)
Ensemble Methods: Foundations and Algorithms
Zhi-Hua Zhou.
(2012)
A Review On Multi-Label Learning Algorithms
Min-Ling Zhang;Zhi-Hua Zhou.
IEEE Transactions on Knowledge and Data Engineering (2014)
Exploratory Undersampling for Class-Imbalance Learning
Xu-Ying Liu;Jianxin Wu;Zhi-Hua Zhou.
systems man and cybernetics (2009)
Exploratory Under-Sampling for Class-Imbalance Learning
Xu-Ying Liu;Jianxin Wu;Zhi-Hua Zhou.
international conference on data mining (2006)
Isolation Forest
F.T. Liu;Kai Ming Ting;Zhi-Hua Zhou.
international conference on data mining (2008)
Multilabel Neural Networks with Applications to Functional Genomics and Text Categorization
Min-Ling Zhang;Zhi-Hua Zhou.
IEEE Transactions on Knowledge and Data Engineering (2006)
Training cost-sensitive neural networks with methods addressing the class imbalance problem
Zhi-Hua Zhou;Xu-Ying Liu.
IEEE Transactions on Knowledge and Data Engineering (2006)
Frontiers of Computer Science
(Impact Factor: 2.669)
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