Add to ORKGDescription This package implements extensions to Freund and Schapire’s AdaBoost algorithm and Friedman’s gradient boosting machine. Includes regression methods for least squares, absolute loss, quantile regression, logistic, Poisson, Cox proportional hazards partial likelihood, and AdaBoost exponential loss. License GPL (version 2 or newer
The use of generalized additive models in statistical data analysis suffers from the restriction to ...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
Several methods for bootstrapping generalized linear regression models are introduced. One-step tech...
Description This package implements extensions to Freund and Schapire's AdaBoost algorithm and ...
Boosting takes on various forms with different programs using different loss functions, different ba...
We present a statistical perspective on boosting. Special emphasis is given to estimating potentiall...
Description Extended techniques for generalized linear models (GLMs), especially for binary re-spons...
Description Distributed gradient boosting based on the mboost package. The parboost package is desig...
Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) fo...
• Boosting is a simple but versatile iterative stepwise gradient descent algorithm. • Versatility: E...
Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) fo...
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...
Description This package provides Partial least squares Regression for (weighted) generalized lin-ea...
Boosting, or boosted regression, is a recent data mining technique that has shown considerable succe...
The use of generalized additive models in statistical data analysis suffers from the restriction to ...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
Several methods for bootstrapping generalized linear regression models are introduced. One-step tech...
Description This package implements extensions to Freund and Schapire's AdaBoost algorithm and ...
Boosting takes on various forms with different programs using different loss functions, different ba...
We present a statistical perspective on boosting. Special emphasis is given to estimating potentiall...
Description Extended techniques for generalized linear models (GLMs), especially for binary re-spons...
Description Distributed gradient boosting based on the mboost package. The parboost package is desig...
Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) fo...
• Boosting is a simple but versatile iterative stepwise gradient descent algorithm. • Versatility: E...
Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) fo...
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...
Description This package provides Partial least squares Regression for (weighted) generalized lin-ea...
Boosting, or boosted regression, is a recent data mining technique that has shown considerable succe...
The use of generalized additive models in statistical data analysis suffers from the restriction to ...
Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Model...
Several methods for bootstrapping generalized linear regression models are introduced. One-step tech...