What is he best known for?
The fields of study he is best known for:
- Mathematical analysis
- Algebra
- Real number
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 most cited work include:
- Interpolation of scattered data: Distance matrices and conditionally positive definite functions (1270 citations)
- Learning Multiple Tasks with Kernel Methods (768 citations)
- Stationary Subdivision (576 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Applied mathematics (27.02%)
- Mathematical analysis (25.00%)
- Discrete mathematics (18.15%)
What were the highlights of his more recent work (between 2009-2020)?
- Mathematical analysis (25.00%)
- Applied mathematics (27.02%)
- Algorithm (13.71%)
In recent papers he was focusing on the following fields of study:
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
- Translation, which have a strong connection to Discrete mathematics,
- Data set which is related to area like Combinatorics.
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.
Between 2009 and 2020, his most popular works were:
- Proximity algorithms for image models: denoising (169 citations)
- Proximity algorithms for image models: denoising (169 citations)
- On Spectral Learning (60 citations)
In his most recent research, the most cited papers focused on:
- Mathematical analysis
- Real number
- Algebra
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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