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Made it consistent across the README
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Will Stanton 2015-07-22 10:37:49 -06:00
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@ -6,7 +6,7 @@ DMLC/XGBoost
An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version. An optimized general purpose gradient boosting library. The library is parallelized, and also provides an optimized distributed version.
It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data It implements machine learning algorithms under the [Gradient Boosting](https://en.wikipedia.org/wiki/Gradient_boosting) framework, including [Generalized Linear Model](https://en.wikipedia.org/wiki/Generalized_linear_model) (GLM) and [Gradient Boosted Decision Trees](https://en.wikipedia.org/wiki/Gradient_boosting#Gradient_tree_boosting) (GBDT). XGBoost can also be [distributed](#features) and scale to Terascale data
Checkout our [Committers and Contributors](CONTRIBUTORS.md) who help make xgboost better. Check out our [Committers and Contributors](CONTRIBUTORS.md) who help make xgboost better.
Documentation: [Documentation of dmlc/xgboost](doc/README.md) Documentation: [Documentation of dmlc/xgboost](doc/README.md)
@ -27,7 +27,7 @@ XGBoost is part of [Distributed Machine Learning Common](http://dmlc.github.io/)
What's New What's New
========== ==========
* XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance) * XGBoost helps Chenglong Chen to win [Kaggle CrowdFlower Competition](https://www.kaggle.com/c/crowdflower-search-relevance)
- Checkout the winning solution at [Highlight links](doc/README.md#highlight-links) - Check out the winning solution at [Highlight links](doc/README.md#highlight-links)
* XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04) * XGBoost-0.4 release, see [CHANGES.md](CHANGES.md#xgboost-04)
* XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing) * XGBoost helps three champion teams to win [WWW2015 Microsoft Malware Classification Challenge (BIG 2015)](http://www.kaggle.com/c/malware-classification/forums/t/13490/say-no-to-overfitting-approaches-sharing)
- Check out the winning solution at [Highlight links](doc/README.md#highlight-links) - Check out the winning solution at [Highlight links](doc/README.md#highlight-links)