His primary areas of investigation include Artificial intelligence, Data mining, Machine learning, Data stream mining and Classifier. The Artificial intelligence study combines topics in areas such as Algorithm design, Graph and Pattern recognition. His studies deal with areas such as Embedding, Data quality, Information retrieval and Leverage as well as Data mining.
His work in the fields of Machine learning, such as Ensemble learning and Semi-supervised learning, intersects with other areas such as Proactive learning and Active learning. His studies in Data stream mining integrate themes in fields like Data modeling, Data stream, Data collection and Data pre-processing. He has included themes like Disjoint sets and Group method of data handling in his Classifier study.
Xingquan Zhu mainly focuses on Artificial intelligence, Machine learning, Data mining, Theoretical computer science and Pattern recognition. His Artificial intelligence research is multidisciplinary, relying on both Computer vision and Graph. The Machine learning study combines topics in areas such as Data modeling and Training set.
Xingquan Zhu specializes in Data mining, namely Data stream mining. His studies deal with areas such as Artificial neural network, Stochastic gradient descent, Graph, Embedding and Feature learning as well as Theoretical computer science. His biological study spans a wide range of topics, including Video tracking, Video processing and Image retrieval.
The scientist’s investigation covers issues in Artificial intelligence, Theoretical computer science, Feature learning, Graph and Graph. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Natural language processing, Machine learning and Pattern recognition. His research in the fields of Support vector machine overlaps with other disciplines such as Information technology.
His Theoretical computer science study incorporates themes from Artificial neural network, Stochastic gradient descent, Node, Embedding and Nearest neighbor search. He interconnects Probabilistic logic, Task analysis and Feature in the investigation of issues within Feature learning. He usually deals with Graph and limits it to topics linked to Feature extraction and Classifier.
His main research concerns Artificial intelligence, Graph, Theoretical computer science, Machine learning and Pattern recognition. His Artificial intelligence study typically links adjacent topics like Algorithm design. His work on Feature vector as part of general Machine learning research is frequently linked to Information technology, bridging the gap between disciplines.
His Pattern recognition research includes elements of Graph classification and Time series. The study incorporates disciplines such as Data mining and Benchmark in addition to Flexibility. His Data mining research is multidisciplinary, incorporating perspectives in Watermark and Data security.
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.
Data mining with big data
Xindong Wu;Xingquan Zhu;Gong-Qing Wu;Wei Ding.
IEEE Transactions on Knowledge and Data Engineering (2014)
Class noise vs. attribute noise: a quantitative study of their impacts
Xingquan Zhu;Xindong Wu.
Artificial Intelligence Review (2003)
Network Representation Learning: A Survey
Daokun Zhang;Jie Yin;Xingquan Zhu;Chengqi Zhang.
IEEE Transactions on Big Data (2020)
Machine Learning for Android Malware Detection Using Permission and API Calls
Naser Peiravian;Xingquan Zhu.
international conference on tools with artificial intelligence (2013)
Tri-party deep network representation
Shirui Pan;Jia Wu;Xingquan Zhu;Chengqi Zhang.
international joint conference on artificial intelligence (2016)
A unified framework for semantics and feature based relevance feedback in image retrieval systems
Ye Lu;Chunhui Hu;Xingquan Zhu;HongJiang Zhang.
acm multimedia (2000)
Eliminating class noise in large datasets
Xingquan Zhu;Xindong Wu;Qijun Chen.
international conference on machine learning (2003)
A survey on instance selection for active learning
Yifan Fu;Xingquan Zhu;Bin Li.
Knowledge and Information Systems (2013)
Online Feature Selection with Streaming Features
Xindong Wu;Kui Yu;Wei Ding;Hao Wang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)
Relevance maximizing, iteration minimizing, relevance-feedback, content-based image retrieval (CBIR)
Hong-Jiang Zhang;Zhong Su;Xingquan Zhu.
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