Mathematical analysis, Applied mathematics, Algorithm, Integral equation and Numerical analysis are his primary areas of study. His Mathematical analysis research is multidisciplinary, relying on both Visualization and Galerkin method. His work carried out in the field of Applied mathematics brings together such families of science as Space, Wavelet and Petrov–Galerkin method.
His Algorithm study combines topics in areas such as Numerical range, Image denoising, Convex function, Mathematical optimization and Continuous wavelet transform. His Convex function research integrates issues from Convex conjugate, Norm, Noise reduction and Linear map. The various areas that he examines in his Integral equation study include Rate of convergence, Iterated function, Iterative method and Superconvergence.
Yuesheng Xu mostly deals with Algorithm, Mathematical analysis, Applied mathematics, Mathematical optimization and Integral equation. His research ties Norm and Algorithm together. As a part of the same scientific study, Yuesheng Xu usually deals with the Mathematical analysis, concentrating on Galerkin method and frequently concerns with Numerical integration.
His Applied mathematics research also works with subjects such as
The scientist’s investigation covers issues in Algorithm, Fixed point, Iterative reconstruction, Artificial intelligence and Optimization problem. Yuesheng Xu combines subjects such as Image processing, Norm and Mathematical optimization with his study of Algorithm. His research investigates the connection between Fixed point and topics such as Minification that intersect with issues in Two stage algorithm.
His biological study spans a wide range of topics, including Image quality, Image resolution, Deblurring, Imaging phantom and Noise reduction. He has researched Optimization problem in several fields, including Rate of convergence, Convex function, Least squares and Convex optimization. The subject of his Numerical analysis research is within the realm of Mathematical analysis.
Yuesheng Xu spends much of his time researching Algorithm, Iterative reconstruction, Mathematical optimization, Multiplicative noise and Reduction. His research in Algorithm is mostly focused on Regularization. His studies in Iterative reconstruction integrate themes in fields like Penalty method, Single-photon emission computed tomography, Mean squared error, Imaging phantom and Image noise.
His study looks at the relationship between Mathematical optimization and fields such as Image quality, as well as how they intersect with chemical problems. The various areas that he examines in his Reduction study include Polynomial, Piecewise linear function, Discrete system, Integral equation and Piecewise. His Convex function research is multidisciplinary, incorporating perspectives in Optimization problem, Fixed point and Rate of convergence.
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.
Charles A. Micchelli;Yuesheng Xu;Haizhang Zhang.
The Journal of Machine Learning Research archive (2006)
Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum
B. Liu;S. Riemenschneider;Y. Xu.
Mechanical Systems and Signal Processing (2006)
A B-spline approach for empirical mode decompositions
Qiuhui Chen;Norden E. Huang;Sherman D. Riemenschneider;Yuesheng Xu.
Advances in Computational Mathematics (2006)
Proximity algorithms for image models: denoising
Charles A. Micchelli;Charles A. Micchelli;Lixin Shen;Yuesheng Xu;Yuesheng Xu.
Inverse Problems (2011)
Wavelet analysis and applications
Icwaa;Tao Qian;Mang I Vai;Yuesheng Xu.
Using the Matrix Refinement Equation for the Construction of Wavelets on Invariant Sets
Charles A. Micchelli;Yuesheng Xu.
Applied and Computational Harmonic Analysis (1994)
Deterministic convergence of an online gradient method for BP neural networks
Wei Wu;Guorui Feng;Zhengxue Li;Yuesheng Xu.
IEEE Transactions on Neural Networks (2005)
Two-dimensional empirical mode decomposition by finite elements
Y Xu;B Liu;J Liu;S Riemenschneider.
Proceedings of The Royal Society A: Mathematical, Physical and Engineering Sciences (2006)
Reproducing Kernel Banach Spaces for Machine Learning
Haizhang Zhang;Yuesheng Xu;Jun Zhang.
Journal of Machine Learning Research (2009)
Gauss-type quadratures for weakly singular integrals and their application to Fredholm integral equations of the second kind
Hideaki Kaneko;Yuesheng Xu.
Mathematics of Computation (1994)
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