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add bibliography

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El Potaeto 2015-02-12 17:19:11 +01:00
parent 8a8eb33114
commit 7f71cc12f4
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R-package/vignettes/xgboostPresentation.Rmd
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@ -5,6 +5,7 @@ output:
css: vignette.css css: vignette.css
number_sections: yes number_sections: yes
toc: yes toc: yes
bibliography: xgboost.bib
vignette: > vignette: >
%\VignetteIndexEntry{Xgboost presentation} %\VignetteIndexEntry{Xgboost presentation}
%\VignetteEngine{knitr::rmarkdown} %\VignetteEngine{knitr::rmarkdown}
@ -18,7 +19,7 @@ This is an introductory document of using the \verb@xgboost@ package in **R**.
**Xgboost** is short for e**X**treme **G**radient **B**oosting package. **Xgboost** is short for e**X**treme **G**radient **B**oosting package.
It is an efficient and scalable implementation of gradient boosting framework by \citep{friedman2001greedy}. It is an efficient and scalable implementation of gradient boosting framework by @friedman2001greedy.
The package includes efficient linear model solver and tree learning algorithm. It supports various objective functions, including *regression*, *classification* and *ranking*. The package is made to be extendible, so that users are also allowed to define their own objectives easily. The package includes efficient linear model solver and tree learning algorithm. It supports various objective functions, including *regression*, *classification* and *ranking*. The package is made to be extendible, so that users are also allowed to define their own objectives easily.
@ -61,7 +62,7 @@ For the purpose of this tutorial we will load the required package.
require(xgboost) require(xgboost)
``` ```
In this example, we are aiming to predict whether a mushroom can be eated (yeah, as always, example data are super interesting :-). In this example, we are aiming to predict whether a mushroom can be eated (yeah, as always, example data are super interesting :-). Mushroom data is cited from UCI Machine Learning Repository. @Bache+Lichman:2013.
Learning Learning
======== ========