Large-scale operator-valued kernel regression. (Régression à noyaux à valeurs opérateurs pour grands ensembles de données)
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Summary
This thesis proposes and study scalable methods to perform regression with Operator-Valued Kernels, and develops a general framework devoted to the approximation of shift-invariant MErcer kernels on Locally Compact Abelian groups.
- Type
- preprint
- Published
- 2017-07-03
- Cited by
- 1
- References
- 182
- Access
- Open access
- OpenAlex
- https://openalex.org/W2738999807
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:125721020
Keywords
Operator (biology), Scalar (mathematics), Bounding overwatch, Hilbert space, Kernel (algebra)
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- Kernel Methods for Pattern Analysis
- Gene selection and classification of microarray data using random forest
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- Anomaly Detection over Noisy Data using Learned Probability Distributions
- A course in abstract harmonic analysis
- Numerical solution of generalized Lyapunov equations
- On weak* convergence in ¹
- Reproducing kernel Hilbert spaces in probability and statistics
- The concentration of measure phenomenon
- Bi-CGSTAB as an induced dimension reduction method
- Random Forests: some methodological insights
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