scientific article; zbMATH DE number 6982938
From MaRDI portal
Publication:4558508
zbMath1468.68158MaRDI QIDQ4558508
Publication date: 22 November 2018
Full work available at URL: http://jmlr.csail.mit.edu/papers/v18/17-343.html
Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of mathematical programming (90C90) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (10)
A review of distributed statistical inference ⋮ Partitioned Approach for High-dimensional Confidence Intervals with Large Split Sizes ⋮ Unnamed Item ⋮ The backbone method for ultra-high dimensional sparse machine learning ⋮ Distributed estimation with empirical likelihood ⋮ Distributed learning for sketched kernel regression ⋮ Robust distributed multicategory angle-based classification for massive data ⋮ A communication efficient distributed one-step estimation ⋮ Debiased magnitude-preserving ranking: learning rate and bias characterization ⋮ Unnamed Item
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- On asymptotically optimal confidence regions and tests for high-dimensional models
- Nearly unbiased variable selection under minimax concave penalty
- The Adaptive Lasso and Its Oracle Properties
- A partially linear framework for massive heterogeneous data
- Asymptotic normality of Powell's kernel estimator
- Oracle properties of SCAD-penalized support vector machine
- Oracle inequalities in empirical risk minimization and sparse recovery problems. École d'Été de Probabilités de Saint-Flour XXXVIII-2008.
- Fast rates for support vector machines using Gaussian kernels
- A note on margin-based loss functions in classification
- Statistical behavior and consistency of classification methods based on convex risk minimization.
- Support-vector networks
- Weak convergence and empirical processes. With applications to statistics
- Statistical performance of support vector machines
- High-dimensional generalized linear models and the lasso
- \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
- Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
- Asymptotic Equivalence of Regularization Methods in Thresholded Parameter Space
- A Constrainedℓ1Minimization Approach to Sparse Precision Matrix Estimation
- Consistency of Support Vector Machines and Other Regularized Kernel Classifiers
- Inverses of Band Matrices and Local Convergence of Spline Projections
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- Variable Selection for Support Vector Machines in Moderately High Dimensions
- High Dimensional Thresholded Regression and Shrinkage Effect
- Convexity, Classification, and Risk Bounds
This page was built for publication: