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, 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.
His most cited work include:
- Top 10 algorithms in data mining (3313 citations)
- ML-KNN: A lazy learning approach to multi-label learning (1869 citations)
- Ensembling neural networks: many could be better than all (1462 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (60.13%)
- Machine learning (40.51%)
- Pattern recognition (17.07%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (60.13%)
- Machine learning (40.51%)
- Mathematical optimization (10.85%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- A brief introduction to weakly supervised learning (450 citations)
- Multi-Label Learning with Global and Local Label Correlation (79 citations)
- Abductive learning: towards bridging machine learning and logical reasoning (45 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
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.
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