Charles A. Micchelli mainly focuses on Applied mathematics, Discrete mathematics, Algebra, Mathematical analysis and Kernel embedding of distributions. His studies deal with areas such as Matrix, Mathematical optimization, Approximation algorithm, Function and Approximation theory as well as Applied mathematics. His Discrete mathematics research is multidisciplinary, incorporating elements of Pure mathematics and Nearest-neighbor interpolation, Spline interpolation, Stairstep interpolation, Interpolation.
Charles A. Micchelli has researched Algebra in several fields, including Reproducing kernel Hilbert space, Hilbert space, Local linear and Calculus. The Mathematical analysis study combines topics in areas such as Wavelet, Galerkin method and Degree. His work in Kernel embedding of distributions addresses issues such as Polynomial kernel, which are connected to fields such as Radial basis function kernel.
His primary areas of study are Applied mathematics, Mathematical analysis, Discrete mathematics, Combinatorics and Algebra. His research integrates issues of Spline, Mathematical optimization, Multivariate statistics, Function and Spline interpolation in his study of Applied mathematics. His Spline interpolation research also covers Bilinear interpolation and Interpolation studies.
His research in Bilinear interpolation is mostly focused on Trilinear interpolation. In most of his Mathematical analysis studies, his work intersects topics such as Wavelet. His Discrete mathematics study incorporates themes from Polynomial and Pure mathematics.
His scientific interests lie mostly in Mathematical analysis, Applied mathematics, Algorithm, Convex function and Mathematical optimization. Charles A. Micchelli studied Mathematical analysis and Function that intersect with Rate of convergence, Linear combination and Multivariate statistics. His study on Multivariate statistics also encompasses disciplines like
His Applied mathematics research incorporates elements of Galerkin method, Volterra integral equation, Integral equation, Fredholm integral equation and Basis function. His study in Algorithm is interdisciplinary in nature, drawing from both Noise removal, Norm, Noise reduction and Tv model. Charles A. Micchelli has included themes like Regularization, Lasso and Linear map in his Convex function study.
The scientist’s investigation covers issues in Mathematical analysis, Algorithm, Mathematical optimization, Noise reduction and Function. Mathematical analysis is frequently linked to Bilinear interpolation in his study. The various areas that Charles A. Micchelli examines in his Algorithm study include Noise removal, Norm, Tv model and Feature selection.
His work deals with themes such as Polynomial kernel, Kernel method, Proximal gradient methods for learning and Compressed sensing, which intersect with Mathematical optimization. His work carried out in the field of Noise reduction brings together such families of science as Convex conjugate and Numerical range. His Function research is multidisciplinary, incorporating perspectives in Reproducing kernel Hilbert space, Kernel, Translation, Multivariate statistics and Unit circle.
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Interpolation of scattered data: Distance matrices and conditionally positive definite functions
Charles A. Micchelli.
Constructive Approximation (1986)
Learning Multiple Tasks with Kernel Methods
Theodoros Evgeniou;Charles A. Micchelli;Massimiliano Pontil.
Journal of Machine Learning Research (2005)
Alfred S. Cavaretta;Charles A. Micchelli;Wolfgang Dahmen.
On Learning Vector-Valued Functions
Charles A. Micchelli;Massimiliano A. Pontil.
Neural Computation (2005)
Charles A. Micchelli;Yuesheng Xu;Haizhang Zhang.
The Journal of Machine Learning Research archive (2006)
Learning the Kernel Function via Regularization
Charles A. Micchelli;Massimiliano Pontil.
Journal of Machine Learning Research (2005)
Using the refinement equations for the construction of Pre-Wavelets II: powers and two
Rong-Qing Jia;Charles A. Micchelli.
Curves and surfaces (1991)
A Survey of Optimal Recovery
C. A. Micchelli;T. J. Rivlin.
Approximation by superposition of sigmoidal and radial basis functions
H.N Mhaskar;Charles A Micchelli.
Advances in Applied Mathematics (1992)
Using the refinement equation for evaluating integrals of wavelets
Wolfgang Dahmen;Charles A. Micchelli.
SIAM Journal on Numerical Analysis (1993)
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