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Xindong Wu
Researcher at Hefei University of Technology
Publications - 612
Citations - 32456
Xindong Wu is an academic researcher from Hefei University of Technology. The author has contributed to research in topics: Feature selection & Computer science. The author has an hindex of 64, co-authored 571 publications receiving 26351 citations. Previous affiliations of Xindong Wu include University of Louisiana at Lafayette & Hong Kong Polytechnic University.
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Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
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Object Detection With Deep Learning: A Review
TL;DR: In this article, a review of deep learning-based object detection frameworks is provided, focusing on typical generic object detection architectures along with some modifications and useful tricks to improve detection performance further.
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Data mining with big data
TL;DR: A HACE theorem is presented that characterizes the features of the Big Data revolution, and a Big Data processing model is proposed, from the data mining perspective, which involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations.
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General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
TL;DR: A general tensor discriminant analysis (GTDA) is developed as a preprocessing step for LDA for face recognition and achieves good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database.
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Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval
TL;DR: An asymmetric bagging and random subspace SVM (ABRS-SVM) is built to solve three problems and further improve the relevance feedback performance.