## 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

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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

### 最新内容

Background: An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria. Objective: To determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. Methods: A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-curve. Results: The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant. Conclusion: Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding MS diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.

### 最新论文

Background: An early diagnosis together with an accurate disease progression monitoring of multiple sclerosis is an important component of successful disease management. Prior studies have established that multiple sclerosis is correlated with speech discrepancies. Early research using objective acoustic measurements has discovered measurable dysarthria. Objective: To determine the potential clinical utility of machine learning and deep learning/AI approaches for the aiding of diagnosis, biomarker extraction and progression monitoring of multiple sclerosis using speech recordings. Methods: A corpus of 65 MS-positive and 66 healthy individuals reading the same text aloud was used for targeted acoustic feature extraction utilizing automatic phoneme segmentation. A series of binary classification models was trained, tuned, and evaluated regarding their Accuracy and area-under-curve. Results: The Random Forest model performed best, achieving an Accuracy of 0.82 on the validation dataset and an area-under-curve of 0.76 across 5 k-fold cycles on the training dataset. 5 out of 7 acoustic features were statistically significant. Conclusion: Machine learning and artificial intelligence in automatic analyses of voice recordings for aiding MS diagnosis and progression tracking seems promising. Further clinical validation of these methods and their mapping onto multiple sclerosis progression is needed, as well as a validating utility for English-speaking populations.

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