## 基础入门

1.Bagging及随机森林 作者：王大宝的CD http://blog.csdn.net/sinat_22594309/article/details/60465700

2.Bagging与随机森林算法原理小结 作者： 刘建平Pinardd http://www.cnblogs.com/pinard/p/6156009.html

3.集成学习：Bagging与随机森林 作者：bigbigship http://blog.csdn.net/bigbigship/article/details/51136985

6.分类器组合方法Bootstrap, Boosting, Bagging, 随机森林（一) 作者：Maggie张张 http://blog.csdn.net/zjsghww/article/details/51591009

7.Bagging与随机森林算法原理小结 作者：6053145618 http://blog.sina.com.cn/s/blog_168cbac120102xbaz.html

8.集成学习（Boosting,Bagging和随机森林) 作者：combatant_yunyun http://blog.csdn.net/u014665416/article/details/51557318

11.集成学习 (AdaBoost、Bagging、随机森林 ) python 预测 作者：江海成 http://blog.csdn.net/qingyang666/article/details/66472981

## 名人主页

### VIP内容

DaRE树利用随机性和缓存来高效删除数据。DaRE树的上层使用随机节点，它均匀随机地选择分割属性和阈值。这些节点很少需要更新，因为它们对数据的依赖性很小。在较低的层次上，选择分割是为了贪婪地优化分割标准，如基尼指数或互信息。DaRE树在每个节点上缓存统计信息，在每个叶子上缓存训练数据，这样当数据被删除时，只更新必要的子树。对于数值属性，贪婪节点在阈值的随机子集上进行优化，以便在逼近最优阈值的同时保持统计量。通过调整贪婪节点的阈值数量和随机节点的数量，DaRE树可以在更准确的预测和更有效的更新之间进行权衡。

https://icml.cc/Conferences/2021/Schedule?showEvent=10523

### 最新论文

Although Deep Neural Networks (DNNs) achieve excellent performance on many real-world tasks, they are highly vulnerable to adversarial attacks. A leading defense against such attacks is adversarial training, a technique in which a DNN is trained to be robust to adversarial attacks by introducing adversarial noise to its input. This procedure is effective but must be done during the training phase. In this work, we propose Augmented Random Forest (ARF), a simple and easy-to-use strategy for robustifying an existing pretrained DNN without modifying its weights. For every image, we generate randomized test time augmentations by applying diverse color, blur, noise, and geometric transforms. Then we use the DNN's logits output to train a simple random forest to predict the real class label. Our method achieves state-of-the-art adversarial robustness on a diversity of white and black box attacks with minimal compromise on the natural images' classification. We test ARF also against numerous adaptive white-box attacks and it shows excellent results when combined with adversarial training. Code is available at https://github.com/giladcohen/ARF.

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