Advertisement

Theory for ℓ1/ℓ2-penalty procedures

  • Peter BühlmannEmail author
  • Sara van de Geer
Chapter
Part of the Springer Series in Statistics book series (SSS)

Abstract

We study four procedures for regression models with group structure in the parameter vector. The first two are for models with univariate response variable. They are the so-called group Lasso (see Chapter 4), and the smoothed group Lasso for the high-dimensional additive model (see Chapter 5). We also discuss multivariate extensions, namely for the linear model with time-varying coefficients, for multivariate regression, and multitask learning.

Keywords

Regularization Parameter Empirical Process Error Loss Truncation Level Group Lasso 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Seminar for StatisticsETH ZürichZürichSwitzerland

Personalised recommendations