Author: Torsten Hothorn, Peter Buhlmann, Thomas Kneib, Matthias Schmid and
Benjamin Hofner
Title: Model-Based Boosting
Description: Functional gradient descent algorithms
(boosting) for optimizing general loss functions utilizing
componentwise least squares, either of parametric linear form or
smoothing splines, or regression trees as base learners for fitting
generalized linear, additive and interaction models to potentially
high-dimensional data.
Diff between mboost versions 1.0-4 dated 2008-11-13 and 1.0-5 dated 2008-12-02
DESCRIPTION | 7 +++---- R/crossvalidation.R | 2 -- R/gamboost.R | 2 -- inst/CHANGES | 5 +++++ inst/doc/SurvivalEnsembles.pdf |binary inst/doc/mboost_illustrations.pdf |binary svn-commit.tmp |only tests/setup.Rout.save |only 8 files changed, 8 insertions(+), 8 deletions(-)