Qing He focuses on Artificial intelligence, Machine learning, Transfer of learning, Data mining and Supramolecular chemistry. His Artificial intelligence study integrates concerns from other disciplines, such as Computation and Pattern recognition. His work in Machine learning covers topics such as Generalization which are related to areas like Speedup.
His research integrates issues of Domain, Data modeling, Data science and Semi-supervised learning in his study of Transfer of learning. His study in the fields of Apriori algorithm under the domain of Data mining overlaps with other disciplines such as A priori and a posteriori. His Supramolecular chemistry study incorporates themes from Receptor, Primary and Artificial systems.
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Data mining, Pattern recognition and Computer vision. His research is interdisciplinary, bridging the disciplines of Domain and Artificial intelligence. His Machine learning study combines topics from a wide range of disciplines, such as Multi-task learning and Generalization.
His Data mining study incorporates themes from Process, Clustering high-dimensional data, Cluster analysis, Statistical classification and Speedup. Qing He has included themes like Autoencoder and Deep learning in his Feature learning study. His work deals with themes such as CURE data clustering algorithm and Data stream clustering, which intersect with Canopy clustering algorithm.
Qing He mostly deals with Artificial intelligence, Machine learning, Recommender system, Embedding and Data mining. His Artificial intelligence research incorporates themes from Domain, Pattern recognition and Natural language processing. Qing He focuses mostly in the field of Domain, narrowing it down to topics relating to Transfer of learning and, in certain cases, Data science.
Qing He combines subjects such as Probabilistic logic and Bayesian probability with his study of Machine learning. His Recommender system research is multidisciplinary, incorporating elements of Generalization and Function. His Data mining research also works with subjects such as
Qing He mainly focuses on Artificial intelligence, Crystallography, Domain, Artificial neural network and Supramolecular chemistry. Qing He has researched Artificial intelligence in several fields, including Machine learning, Pattern recognition and Natural language processing. His Machine learning research integrates issues from Online advertising, Generator and Meta learning.
His Crystallography research includes themes of Ion, Redox, Pyrrole and Trifluoroacetic acid. In his research, Data mining is intimately related to Database transaction, which falls under the overarching field of Domain. His biological study spans a wide range of topics, including Combinatorial chemistry and Ion pairs.
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A Comprehensive Survey on Transfer Learning
Fuzhen Zhuang;Zhiyuan Qi;Keyu Duan;Dongbo Xi.
Proceedings of the IEEE (2021)
Parallel K-Means Clustering Based on MapReduce
Weizhong Zhao;Huifang Ma;Qing He.
international conference on cloud computing (2009)
Supervised representation learning: transfer learning with deep autoencoders
Fuzhen Zhuang;Xiaohu Cheng;Ping Luo;Sinno Jialin Pan.
international conference on artificial intelligence (2015)
Parallel Implementation of Apriori Algorithm Based on MapReduce
Ning Li;Li Zeng;Qing He;Zhongzhi Shi.
software engineering, artificial intelligence, networking and parallel/distributed computing (2012)
Parallel extreme learning machine for regression based on MapReduce
Qing He;Tianfeng Shang;Fuzhen Zhuang;Zhongzhi Shi.
Extreme support vector machine classifier
Qiuge Liu;Qing He;Zhongzhi Shi.
knowledge discovery and data mining (2008)
Deep Subdomain Adaptation Network for Image Classification
Yongchun Zhu;Fuzhen Zhuang;Jindong Wang;Guolin Ke.
IEEE Transactions on Neural Networks (2021)
A Survey on Knowledge Graph-Based Recommender Systems
Qingyu Guo;Fuzhen Zhuang;Chuan Qin;Hengshu Zhu.
IEEE Transactions on Knowledge and Data Engineering (2020)
Transfer learning from multiple source domains via consensus regularization
Ping Luo;Fuzhen Zhuang;Hui Xiong;Yuhong Xiong.
conference on information and knowledge management (2008)
Learning deep representations via extreme learning machines
Wenchao Yu;Fuzhen Zhuang;Qing He;Zhongzhi Shi.
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