高维空间中基于模型的提升算法
Model-based boosting in high dimensions.
作者信息
Hothorn Torsten, Bühlmann Peter
机构信息
Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg Waldstrasse 6, D-91054 Erlangen, Germany.
出版信息
Bioinformatics. 2006 Nov 15;22(22):2828-9. doi: 10.1093/bioinformatics/btl462. Epub 2006 Aug 29.
SUMMARY
The R add-on package mboost implements 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.
AVAILABILITY
Package mboost is available from the Comprehensive R Archive Network (http://CRAN.R-project.org) under the terms of the General Public Licence (GPL).
摘要
R 附加包 mboost 实现了功能梯度下降算法（提升法），用于利用逐分量最小二乘法优化一般损失函数，其中逐分量最小二乘法采用参数线性形式或平滑样条，或者以回归树作为基学习器，以便将广义线性模型、加法模型和交互模型拟合到潜在的高维数据。
可用性
包 mboost 可根据通用公共许可证（GPL）条款从综合 R 存档网络（http://CRAN.R-project.org）获取。
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