CONVERGENCE ANALYSIS OF COEFFICIENT-BASED REGULARIZATION UNDER MOMENT INCREMENTAL CONDITION
From MaRDI portal
Publication:2874064
DOI10.1142/S0219691314500088zbMath1296.68149MaRDI QIDQ2874064
Publication date: 28 January 2014
Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)
General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05)
Related Items (5)
Statistical consistency of coefficient-based conditional quantile regression ⋮ Learning rates for the kernel regularized regression with a differentiable strongly convex loss ⋮ Support vector machines regression with unbounded sampling ⋮ Constructive analysis for least squares regression with generalized \(K\)-norm regularization ⋮ Constructive analysis for coefficient regularization regression algorithms
Cites Work
- Optimal learning rates for least squares regularized regression with unbounded sampling
- Least square regression with indefinite kernels and coefficient regularization
- Unified approach to coefficient-based regularized regression
- Multi-kernel regularized classifiers
- Learning theory estimates for coefficient-based regularized regression
- Learning with sample dependent hypothesis spaces
- Learning rates of least-square regularized regression
- Learning theory estimates via integral operators and their approximations
- COMPRESSED SENSING BY ITERATIVE THRESHOLDING OF GEOMETRIC WAVELETS: A COMPARING STUDY
- Learning Theory
- Capacity of reproducing kernel spaces in learning theory
- ON AN EFFICIENT SPARSE REPRESENTATION OF OBJECTS OF GENERAL SHAPE VIA CONTINUOUS EXTENSION AND WAVELET APPROXIMATION
- ONLINE LEARNING WITH MARKOV SAMPLING
- ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
- SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
This page was built for publication: CONVERGENCE ANALYSIS OF COEFFICIENT-BASED REGULARIZATION UNDER MOMENT INCREMENTAL CONDITION