update doc for v2

README.md

@@ -16,6 +16,8 @@ For more details, please refer to [Features](https://github.com/Microsoft/LightG
 
 News
 ----
+02/20/2017 : Update to LightGBM v2. Add two new features: Gradient-based One-Side Sampling(GOSS) and Exclusive Feature Bundling(EFB). With GOSS and EFB, LightGBM can further speed up the training. Details are avaliable in [Features](https://github.com/Microsoft/LightGBM/wiki/Features).
+
 01/08/2017 : Release [**R-package**](./R-package) beta version, welcome to have a try and provide feedback.
 
 12/05/2016 : [deprecated in v2]**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 (refer to [categorical log]( https://github.com/guolinke/boosting_tree_benchmarks/blob/master/lightgbm/lightgbm_dataexpo_speed.log) and [one-hot log]( https://github.com/guolinke/boosting_tree_benchmarks/blob/master/lightgbm/lightgbm_dataexpo_onehot_speed.log)).

docs/Parameters.md

@@ -29,6 +29,7 @@ The parameter format is ```key1=value1 key2=value2 ... ``` . And parameters can
 * ```boosting```, default=```gbdt```, type=enum, options=```gbdt```,```dart```, alias=```boost```,```boosting_type```
   * ```gbdt```, traditional Gradient Boosting Decision Tree 
   * ```dart```, [Dropouts meet Multiple Additive Regression Trees](https://arxiv.org/abs/1505.01866)
+  * ```goss```, Gradient-based One-Side Sampling
 * ```data```, default=```""```, type=string, alias=```train```,```train_data```
   * training data, LightGBM will train from this data
 * ```valid```, default=```""```, type=multi-string, alias=```test```,```valid_data```,```test_data```
@@ -95,6 +96,10 @@ The parameter format is ```key1=value1 key2=value2 ... ``` . And parameters can
   * only used in ```dart```, true if want to use xgboost dart mode
 * ```drop_seed```, default=```4```, type=int
   * only used in ```dart```, used to random seed to choose dropping models.
+* ```top_rate```, default=```0.2```, type=double
+  * only used in ```goss```,  the retain ratio of large gradient data
+* ```other_rate```, default=```0.1```, type=int
+  * only used in ```goss```,  the retain ratio of small gradient data
 
 
 ## IO parameters