2017 - ACM Fellow For contributions to artificial intelligence and data mining
2013 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to fundamental research and practical applications of AI planning, data mining and case-based reasoning
2012 - Fellow of the American Association for the Advancement of Science (AAAS)
2012 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to data mining and transfer learning
2011 - ACM Distinguished Member
2009 - IEEE Fellow For contributions to understanding and application of intelligent planning, learning and data mining
Qiang Yang mainly focuses on Artificial intelligence, Machine learning, Data mining, Transfer of learning and Pattern recognition. His Artificial intelligence research integrates issues from Domain and Multi-task learning, Task. His study in Multi-task learning is interdisciplinary in nature, drawing from both Active learning, Instance-based learning and Feature.
His study in Collaborative filtering, Support vector machine, Unsupervised learning, Labeled data and Decision tree is carried out as part of his Machine learning studies. His research on Data mining often connects related topics like Regression analysis. His Transfer of learning research is multidisciplinary, incorporating perspectives in Contextual image classification, Knowledge transfer and Feature vector.
Qiang Yang mainly investigates Artificial intelligence, Machine learning, Data mining, Transfer of learning and Information retrieval. His research integrates issues of Domain, Task and Pattern recognition in his study of Artificial intelligence. His work deals with themes such as Multi-task learning and Knowledge transfer, which intersect with Machine learning.
Transfer of learning is frequently linked to Recommender system in his study.
His main research concerns Artificial intelligence, Machine learning, Transfer of learning, Photovoltaic system and Deep learning. Qiang Yang interconnects Domain and Pattern recognition in the investigation of issues within Artificial intelligence. The various areas that he examines in his Machine learning study include Task and Benchmark.
His Transfer of learning research is multidisciplinary, incorporating elements of Recommender system and Representation. He has researched Photovoltaic system in several fields, including Real-time computing and Mathematical optimization. His study looks at the relationship between Mathematical optimization and fields such as Distributed generation, as well as how they intersect with chemical problems.
Artificial intelligence, Machine learning, Transfer of learning, Deep learning and Domain are his primary areas of study. The concepts of his Artificial intelligence study are interwoven with issues in Task and Pattern recognition. His work carried out in the field of Machine learning brings together such families of science as Representation, Knowledge transfer and Benchmark.
His Transfer of learning research incorporates elements of Recommender system, Feature learning and Service. His studies in Deep learning integrate themes in fields like Natural language processing, Edge computing, Data mining and Graph. He combines subjects such as Sentiment analysis and Transfer with his study of Domain.
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.
A Survey on Transfer Learning
Sinno Jialin Pan;Qiang Yang.
IEEE Transactions on Knowledge and Data Engineering (2010)
Top 10 algorithms in data mining
Xindong Wu;Vipin Kumar;J. Ross Quinlan;Joydeep Ghosh.
Knowledge and Information Systems (2007)
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
Shuicheng Yan;Dong Xu;Benyu Zhang;Hong-Jiang Zhang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2007)
Domain Adaptation via Transfer Component Analysis
Sinno Jialin Pan;Ivor W Tsang;James T Kwok;Qiang Yang.
IEEE Transactions on Neural Networks (2011)
Boosting for transfer learning
Wenyuan Dai;Qiang Yang;Gui-Rong Xue;Yong Yu.
international conference on machine learning (2007)
EigenTransfer: a unified framework for transfer learning
Wenyuan Dai;Ou Jin;Gui-Rong Xue;Qiang Yang.
international conference on machine learning (2009)
One-Class Collaborative Filtering
Rong Pan;Yunhong Zhou;Bin Cao;N.N. Liu.
international conference on data mining (2008)
Scalable collaborative filtering using cluster-based smoothing
Gui-Rong Xue;Chenxi Lin;Qiang Yang;WenSi Xi.
international acm sigir conference on research and development in information retrieval (2005)
10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH
Qiang Yang;Xindong Wu.
International Journal of Information Technology and Decision Making (2006)
Collaborative location and activity recommendations with GPS history data
Vincent W. Zheng;Yu Zheng;Xing Xie;Qiang Yang.
the web conference (2010)
Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.
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