Yun Yang

Yun YangDr Yang has a very solid and broad knowledge and experience in computer science, and in-depth expertise in machine learning, data mining and temporal data processing. His main research area is in the temporal data mining and unsupervised ensemble leaning. In these topics, he has produced some internationally excellent research results including proposing and developing several innovation methods and algorithms. These works have been published in the international leading research journals or conferences such as IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Systems, Man, and Cybernetics- Part C, and Knowledge-Based Systems. His research results have attracted a lot of attentions from the machine learning research community and made the significant impact. As an evidence to illustrate the attention that his work has received and the impact his work has produced, his IEEE Transaction publication "Temporal data clustering via weighted clustering ensemble with different representations" has been cited more than 42 times based on Google scholar. Read More Read Less

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Green Space and Human Health in China21 %
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Biotechnologies for Gene Therapy37 %
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Atlas of Nuclear Medicine in Musculoskeletal System21 %
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Biotechnologies for Gene Therapy37 %
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Atlas of Nuclear Medicine in Musculoskeletal System
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Simulation Method of Multipactor and Its Application in Satellite Microwave Components41 %
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Polarization Remote Sensing Physics37 %
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Polarization Remote Sensing Physics37 %
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The Way of the Barbarians34 %
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The Way of the Barbarians31 %
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Advances in Graphic Communication, Printing and Packaging37 %
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Applied Sciences in Graphic Communication and Packaging37 %
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Advanced Graphic Communications, Packaging Technology and Materials37 %
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Intelligent Data Engineering and Automated Learning – IDEAL 201636 %
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The Design of Cloud Workflow Systems36 %
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01 Nov 2011
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Temporal QOS Management in Scientific Cloud Workflow Systems53 %
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20 Feb 2012
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Temporal Data Mining via Unsupervised Ensemble Learning45 %
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Applied Sciences in Graphic Communication and Packaging37 %
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Advanced Graphic Communications, Packaging Technology and Materials37 %
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Computation and Storage in the Cloud45 %
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