Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting
نویسندگان
چکیده
منابع مشابه
Comment: Boosting Algorithms: Regularization, Prediction and Model Fitting
We congratulate the authors (hereafter BH) for an interesting take on the boosting technology, and for developing a modular computational environment in R for exploring their models. Their use of low-degree-offreedom smoothing splines as a base learner provides an interesting approach to adaptive additive modeling. The notion of “Twin Boosting” is interesting as well; besides the adaptive lasso...
متن کاملComment: Boosting Algorithms: Regularization, Prediction and Model Fitting
The authors are doing the readers of Statistical Science a true service with a well-written and up-to-date overview of boosting that originated with the seminal algorithms of Freund and Schapire. Equally, we are grateful for high-level software that will permit a larger readership to experiment with, or simply apply, boosting-inspired model fitting. The authors show us a world of methodology th...
متن کاملBoosting Algorithms: Regularization, Prediction and Model Fitting
We 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 selectio...
متن کاملRejoinder: Boosting Algorithms: Regularization, Prediction and Model Fitting
We are grateful that Hastie points out the connection to degrees of freedom for LARS which leads to another—and often better—definition of degrees of freedom for boosting in generalized linear models. As Hastie writes and as we said in the paper, our formula for degrees of freedom is only an approximation: the cost of searching, for example, for the best variable in componentwise linear least s...
متن کاملDiscussion of “ Boosting Algorithms : Regularization , Prediction and Model Fitting ” by Peter Bühlmann and Torsten Hothorn
We congratulate the authors (hereafter BH) for an interesting take on the boosting technology, and for developing a modular computational environment in R for exploring their models. Their use of low-degree-of-freedom smoothing splines as a base learner provides an interesting approach to adaptive additive modeling. The notion of “Twin Boosting” is interesting as well; besides the adaptive lass...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Statistical Science
سال: 2007
ISSN: 0883-4237
DOI: 10.1214/07-sts242a