chg docs

README.md

@@ -5,22 +5,14 @@ It implements machine learning algorithm under gradient boosting framework, incl
 
 Contributors: https://github.com/dmlc/xgboost/graphs/contributors
 
+Documentations: [Documentation of xgboost](doc/README.md)
+
 Issues Tracker: [https://github.com/dmlc/xgboost/issues](https://github.com/dmlc/xgboost/issues?q=is%3Aissue+label%3Aquestion)
 
 Please join [XGBoost User Group](https://groups.google.com/forum/#!forum/xgboost-user/) to ask questions and share your experience on xgboost.
 
-Examples Code: [Learning to use xgboost by examples](demo)
-
 Distributed Version: [Distributed XGBoost](multi-node)
 
-Notes on the Code: [Code Guide](src)
-
-Documentation: [Documentation of xgboost](doc/README.md)
-
-Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)
-* This slide is made by Tianqi Chen to introduce gradient boosting in a statistical view.
-* It present boosted tree learning as formal functional space optimization of defined objective.
-* The model presented is used by xgboost for boosted trees
 
 What's New
 ==========

demo/README.md

@@ -49,3 +49,4 @@ Benchmarks
 ====
 * [Starter script for Kaggle Higgs Boson](kaggle-higgs)
 * [Kaggle Tradeshift winning solution by daxiongshu](https://github.com/daxiongshu/kaggle-tradeshift-winning-solution) 
+

doc/README.md

@@ -1,11 +1,13 @@
 List of Documentations
 ====
-* [Parameters](parameter.md)
 * [Using XGBoost in Python](python.md)
 * [Using XGBoost in R](../R-package/vignettes/xgboostPresentation.Rmd)
 * [Learning to use xgboost by example](../demo)
 * [External Memory Version](external_memory.md)
 * [Text input format](input_format.md)
+* [Notes on the Code](../src)
+* [Parameters](parameter.md)
+* Learning about the model: [Introduction to Boosted Trees](http://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf)
 
 How to get started
 ====