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Youxi Wu
Researcher at Hebei University of Technology
Publications - 88
Citations - 953
Youxi Wu is an academic researcher from Hebei University of Technology. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 12, co-authored 60 publications receiving 503 citations. Previous affiliations of Youxi Wu include University of Vermont.
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Journal ArticleDOI
Classification of Mental Task From EEG Signals Using Immune Feature Weighted Support Vector Machines
TL;DR: In this study, immune feature weighted SVM (IFWSVM) method was proposed and Immune algorithm (IA) was then introduced in searching for the optimal feature weights and the parameters simultaneously in SVM to multiclassify five different mental tasks.
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Mining sequential patterns with periodic wildcard gaps
TL;DR: Two new algorithms, MAPB and MAPD, are proposed to solve the problem effectively with low memory requirements and a heuristic algorithm MAPBOK (MAPB for tOp-K) based on MAPB to deal with the Top-K frequent patterns for each length is designed.
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Subkilometer crater discovery with boosting and transfer learning
Wei Ding,Tomasz F. Stepinski,Yang Mu,Lourenço Bandeira,Ricardo Ricardo,Youxi Wu,Zhenyu Lu,Tianyu Cao,Xindong Wu +8 more
TL;DR: An integrated framework on autodetection of subkilometer craters with boosting and transfer learning that can achieve an F1 score above 0.85, a significant improvement over the other crater detection algorithms.
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NOSEP: Nonoverlapping Sequence Pattern Mining With Gap Constraints
TL;DR: A new Apriori-based nonoverlapping sequence pattern mining algorithm, NOSEP, is proposed, which uses a specially designed data structure, Nettree, to calculate the exact occurrence of a pattern in the sequence.
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Tumor Detection in MR Images Using One-Class Immune Feature Weighted SVMs
TL;DR: In this study, immune algorithm (IA) was introduced in searching for the optimal feature weights and the parameters simultaneously and one-class immune feature weighted SVM (IFWSVM) was proposed to detect tumors in MR images.