His scientific interests lie mostly in Data mining, Artificial intelligence, Data stream mining, Machine learning and Outlier. Jing Gao performs multidisciplinary studies into Data mining and Graph in his work. The various areas that Jing Gao examines in his Artificial intelligence study include Social media and Pattern recognition.
The Data stream mining study combines topics in areas such as Data stream and Training set. His Cluster analysis, Ensemble learning and Recurrent neural network study, which is part of a larger body of work in Machine learning, is frequently linked to Health informatics, bridging the gap between disciplines. He works mostly in the field of Outlier, limiting it down to concerns involving Anomaly detection and, occasionally, Temporal database, Variety and Data set.
Jing Gao spends much of his time researching Artificial intelligence, Machine learning, Data mining, Cluster analysis and Data stream mining. His Artificial intelligence research includes themes of Key and Pattern recognition. His Feature learning and Semi-supervised learning study in the realm of Machine learning interacts with subjects such as Data modeling and Health informatics.
His research in the fields of Anomaly detection overlaps with other disciplines such as Graph. His work in Data stream mining addresses subjects such as Data stream, which are connected to disciplines such as Data stream clustering, Class and Statistical classification. Jing Gao usually deals with Deep learning and limits it to topics linked to Social media and Data science.
His main research concerns Artificial intelligence, Machine learning, Information retrieval, Social media and Deep learning. His biological study focuses on Leverage. His work carried out in the field of Machine learning brings together such families of science as Matching and Benchmark.
His Information retrieval study also includes fields such as
Jing Gao mainly investigates Artificial intelligence, Machine learning, Deep learning, Reliability and Social media. His work carried out in the field of Artificial intelligence brings together such families of science as Mobile device and Pattern recognition. His study on Convolutional neural network is often connected to Health informatics as part of broader study in Machine learning.
In the subject of general Deep learning, his work in Deep belief network is often linked to Reading, thereby combining diverse domains of study. The Reliability study combines topics in areas such as Crowdsourcing and Majority rule. As part of the same scientific family, Jing Gao usually focuses on Social media, concentrating on Fake news and intersecting with Information retrieval, Leverage, Reinforcement learning, Adversarial system and Artificial neural network.
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.
Outlier Detection for Temporal Data: A Survey
Manish Gupta;Jing Gao;Charu C. Aggarwal;Jiawei Han.
IEEE Transactions on Knowledge and Data Engineering (2014)
Multi-view clustering via joint nonnegative matrix factorization
Jing Gao;Jiawei Han;Jialu Liu;Chi Wang.
siam international conference on data mining (2013)
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
Mohammad M Masud;Jing Gao;L Khan;Jiawei Han.
IEEE Transactions on Knowledge and Data Engineering (2011)
Knowledge transfer via multiple model local structure mapping
Jing Gao;Wei Fan;Jing Jiang;Jiawei Han.
knowledge discovery and data mining (2008)
Resolving conflicts in heterogeneous data by truth discovery and source reliability estimation
Qi Li;Yaliang Li;Jing Gao;Bo Zhao.
international conference on management of data (2014)
A Survey on Truth Discovery
Yaliang Li;Jing Gao;Chuishi Meng;Qi Li.
Sigkdd Explorations (2016)
A general framework for mining concept-drifting data streams with skewed distributions
Jing Gao;Wei Fan;Jiawei Han;Philip S. Yu.
siam international conference on data mining (2007)
On community outliers and their efficient detection in information networks
Jing Gao;Feng Liang;Wei Fan;Chi Wang.
knowledge discovery and data mining (2010)
A confidence-aware approach for truth discovery on long-tail data
Qi Li;Yaliang Li;Jing Gao;Lu Su.
very large data bases (2014)
Graph regularized transductive classification on heterogeneous information networks
Ming Ji;Yizhou Sun;Marina Danilevsky;Jiawei Han.
european conference on machine learning (2010)
Profile was last updated on December 6th, 2021.
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