update documents.

README.md

@@ -23,6 +23,9 @@ For more details, please refer to [Features](https://github.com/Microsoft/LightG
 
 News
 ----
+
+08/02/2017: **Optimal split for Categorical Features**. Now LightGBM can provide much better accuracy when using categorical features. Compared with one-hot coding, LightGBM's new solution shows a great improvement.
+
 07/13/2017: [Gitter](https://gitter.im/Microsoft/LightGBM) is avaiable.
 
 06/20/2017: Python-package is on PyPI now.
@@ -31,17 +34,17 @@ News
 
 05/03/2017: LightGBM v2 stable release.
 
-04/10/2017 : LightGBM supports GPU-accelerated tree learning now. Please read our [GPU Tutorial](./docs/GPU-Tutorial.md) and [Performance Comparison](./docs/GPU-Performance.md).
+04/10/2017: LightGBM supports GPU-accelerated tree learning now. Please read our [GPU Tutorial](./docs/GPU-Tutorial.md) and [Performance Comparison](./docs/GPU-Performance.md).
 
-02/20/2017 : Update to LightGBM v2.
+02/20/2017: Update to LightGBM v2.
 
 02/12/2017: LightGBM v1 stable release.
 
-01/08/2017 : Release [**R-package**](./R-package) beta version, welcome to have a try and provide feedback.
+01/08/2017: Release [**R-package**](./R-package) beta version, welcome to have a try and provide feedback.
 
-12/05/2016 : **Categorical Features as input directly**(without one-hot coding). Experiment on [Expo data](http://stat-computing.org/dataexpo/2009/) shows about 8x speed-up with same accuracy compared with one-hot coding.
+12/05/2016: **Categorical Features as input directly**(without one-hot coding). Experiment on [Expo data](http://stat-computing.org/dataexpo/2009/) shows about 8x speed-up with same accuracy compared with one-hot coding.
 
-12/02/2016 : Release [**python-package**](./python-package) beta version, welcome to have a try and provide feedback.
+12/02/2016: Release [**python-package**](./python-package) beta version, welcome to have a try and provide feedback.
 
 
 External (unofficial) Repositories

docs/Quick-Start.md

@@ -14,26 +14,8 @@ Follow the [Installation Guide](./Installation-Guide.md) to install LightGBM fir
 
 LightGBM supports input data file with [CSV](https://en.wikipedia.org/wiki/Comma-separated_values), [TSV](https://en.wikipedia.org/wiki/Tab-separated_values) and [LibSVM](https://www.csie.ntu.edu.tw/~cjlin/libsvm/) formats.
 
-Label is the data of first column, and there is no header in the file.
+Label is the data of first column. (You can also specific label column, weight column and query/group id column. Refer to [io-parameters](./Parameters.md#io-parameters).)
 
-### Categorical feature support
-
-update 12/5/2016:
-
-LightGBM can use categorical feature directly (without one-hot coding). The experiment on [Expo data](http://stat-computing.org/dataexpo/2009/) shows about 8x speed-up compared with one-hot coding.
-
-For the setting details, please refer to [Parameters](./Parameters.md#io-parameters).
-
-### Weight and query/group data
-LightGBM also support weighted training, it needs an additional [weight data](./Parameters.md#weight-data). And it needs an additional [query data](./Parameters.md#query-data) for ranking task.
-
-update 11/3/2016:
-
-1. support input with header now
-2. can specific label column, weight column and query/group id column. Both index and column are supported
-3. can specific a list of ignored columns
-
-For the detailed usage, please refer to [Configuration](./Parameters.md#io-parameters).
 
 ## Parameter quick look