Quantile regression with \(\ell_1\)-regularization and Gaussian kernels
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Publication:457695
DOI10.1007/s10444-013-9317-0zbMath1302.62151OpenAlexW2025774781MaRDI QIDQ457695
J. Herrera, D. Rodríguez-Gómez
Publication date: 29 September 2014
Published in: Advances in Computational Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10444-013-9317-0
learning theoryquantile regression\(\ell_1\)-regularizationGaussian kernelsconcentration estimate for error analysisunbounded sampling processes
Inequalities; stochastic orderings (60E15) General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (4)
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Cites Work
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- Concentration estimates for learning with \(\ell ^{1}\)-regularizer and data dependent hypothesis spaces
- Optimal learning rates for least squares regularized regression with unbounded sampling
- Estimating conditional quantiles with the help of the pinball loss
- Multi-kernel regularized classifiers
- Fast rates for support vector machines using Gaussian kernels
- High order Parzen windows and randomized sampling
- Reproducing kernel Banach spaces with the \(\ell^1\) norm
- Weak convergence and empirical processes. With applications to statistics
- Concentration estimates for learning with unbounded sampling
- Optimal regression rates for SVMs using Gaussian kernels
- Conditional quantiles with varying Gaussians
- Learning with sample dependent hypothesis spaces
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- How to compare different loss functions and their risks
- Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
- Probability Inequalities for the Sum of Independent Random Variables
- Learning Theory
- Support Vector Machines
- Reappraising Medfly Longevity
- ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
- Semiparametric Estimation of Regression Quantiles with Application to Standardizing Weight for Height and Age in US Children
- SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
- Massive data discrimination via linear support vector machines
- Theory of Reproducing Kernels
- Scattered Data Approximation
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