Add to ORKGWe present a statistical perspective on boosting. Special emphasis is given to estimating potentially complex parametric or nonparametric models, including generalized linear and additive models as well as regression models for survival analysis. Concepts of degrees of freedom and corresponding Akaike or Bayesian information criteria, particularly useful for regularization and variable selection in high-dimensional covariate spaces, are discussed as well. The practical aspects of boosting procedures for fitting statistical models are illustrated by means of the dedicated open-source software package mboost. This package implements functions which can be used for model fitting, prediction and variable selection. It is flexible, allowing for ...
Abstract Background Statistical boosting is a computational approach to select and estimate interpre...
Statistical boosting is a powerful tool that has become increasingly more popular in recent years. I...
In biomedical research, boosting-based regression approaches have gained much attention in the last ...
Boosting algorithms were originally developed for machine learning but were later adapted to estimat...
Background: The concept of boosting emerged from the field of machine learning. The basic idea is to...
An accessible introduction and essential reference for an approach to machine learning that creates ...
Boosting is one of the most important methods for fitting regression models and building prediction ...
This diploma thesis focuses on regularization and variable selection in regres- sion models. Basics ...
Boosting is one of the most important methods for fitting regression models and build-ing prediction...
Boosting is a highly flexible and powerful approach when it comes to making predictions in non-param...
We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boos...
Boosting, or boosted regression, is a recent data-mining technique that has shown considerable succe...
Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) fo...
Abstract Background Statistical boosting is a computational approach to select and estimate interpre...
Statistical boosting is a powerful tool that has become increasingly more popular in recent years. I...
In biomedical research, boosting-based regression approaches have gained much attention in the last ...
Boosting algorithms were originally developed for machine learning but were later adapted to estimat...
Background: The concept of boosting emerged from the field of machine learning. The basic idea is to...
An accessible introduction and essential reference for an approach to machine learning that creates ...
Boosting is one of the most important methods for fitting regression models and building prediction ...
This diploma thesis focuses on regularization and variable selection in regres- sion models. Basics ...
Boosting is one of the most important methods for fitting regression models and build-ing prediction...
Boosting is a highly flexible and powerful approach when it comes to making predictions in non-param...
We provide a detailed hands-on tutorial for the R add-on package mboost. The package implements boos...
Boosting, or boosted regression, is a recent data-mining technique that has shown considerable succe...
Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) fo...
Abstract Background Statistical boosting is a computational approach to select and estimate interpre...
Statistical boosting is a powerful tool that has become increasingly more popular in recent years. I...
In biomedical research, boosting-based regression approaches have gained much attention in the last ...