A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Direct generalized additive modeling with penalized likelihood
Computational Statistics & Data Analysis
Prediction games and arcing algorithms
Neural Computation
Knot selection by boosting techniques
Computational Statistics & Data Analysis
The Journal of Machine Learning Research
Consistency of support vector machines using additive kernels for additive models
Computational Statistics & Data Analysis
Geoadditive expectile regression
Computational Statistics & Data Analysis
Model-based boosting in R: a hands-on tutorial using the R package mboost
Computational Statistics
| Hi-index | 0.03 |
An efficient approximation of L"2 Boosting with component-wise smoothing splines is considered. Smoothing spline base-learners are replaced by P-spline base-learners, which yield similar prediction errors but are more advantageous from a computational point of view. A detailed analysis of the effect of various P-spline hyper-parameters on the boosting fit is given. In addition, a new theoretical result on the relationship between the boosting stopping iteration and the step length factor used for shrinking the boosting estimates is derived.