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- Charles A. Micchelli

Discipline name
D-index
D-index (Discipline H-index) only includes papers and citation values for an examined
discipline in contrast to General H-index which accounts for publications across all
disciplines.
Citations
Publications
World Ranking
National Ranking

Mathematics
D-index
66
Citations
15,980
194
World Ranking
171
National Ranking
92

- 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.

- 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)

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.

- Applied mathematics (27.02%)
- Mathematical analysis (25.00%)
- Discrete mathematics (18.15%)

- Mathematical analysis (25.00%)
- Applied mathematics (27.02%)
- Algorithm (13.71%)

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.

- Proximity algorithms for image models: denoising (169 citations)
- Proximity algorithms for image models: denoising (169 citations)
- On Spectral Learning (60 citations)

- 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.

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.

Interpolation of scattered data: Distance matrices and conditionally positive definite functions

Charles A. Micchelli.

Constructive Approximation **(1986)**

2159 Citations

Stationary Subdivision

Alfred S. Cavaretta;Charles A. Micchelli;Wolfgang Dahmen.

**(1991)**

1028 Citations

Learning Multiple Tasks with Kernel Methods

Theodoros Evgeniou;Charles A. Micchelli;Massimiliano Pontil.

Journal of Machine Learning Research **(2005)**

993 Citations

On Learning Vector-Valued Functions

Charles A. Micchelli;Massimiliano A. Pontil.

Neural Computation **(2005)**

519 Citations

Learning the Kernel Function via Regularization

Charles A. Micchelli;Massimiliano Pontil.

Journal of Machine Learning Research **(2005)**

480 Citations

Using the refinement equations for the construction of Pre-Wavelets II: powers and two

Rong-Qing Jia;Charles A. Micchelli.

Curves and surfaces **(1991)**

468 Citations

Universal Kernels

Charles A. Micchelli;Yuesheng Xu;Haizhang Zhang.

The Journal of Machine Learning Research archive **(2006)**

450 Citations

A Survey of Optimal Recovery

C. A. Micchelli;T. J. Rivlin.

**(1977)**

447 Citations

Approximation by superposition of sigmoidal and radial basis functions

H.N Mhaskar;Charles A Micchelli.

Advances in Applied Mathematics **(1992)**

332 Citations

Using the refinement equation for evaluating integrals of wavelets

Wolfgang Dahmen;Charles A. Micchelli.

SIAM Journal on Numerical Analysis **(1993)**

315 Citations

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Publications: 33

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