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两种结合学习者的袋装算法,以鼓励多样性

Two Bagging Algorithms with Coupled Learners to Encourage Diversity
课程网址: http://videolectures.net/ida07_valle_tba/  
主讲教师: Carlos Valle
开课单位: 费德里科圣马技术大学
开课时间: 2007-10-08
课程语种: 英语
中文简介:
在本文中,我们提出了两种集成学习算法,它们利用了增强和超出袋估计,试图​​继承套袋对过度拟合的鲁棒性。对于装袋,使用这些算法,学习者可以看到其他学习者,并合作获得多样性,这一特征已被证明是集合模型主要关注的问题。使用从UCI获得的两个回归问题提供实验。
课程简介: In this paper, we present two ensemble learning algorithms which make use of boostrapping and out-of-bag estimation in an attempt to inherit the robustness of bagging to overfitting. As against bagging, with these algorithms learners have visibility on the other learners and cooperate to get diversity, a characteristic that has proved to be an issue of major concern to ensemble models. Experiments are provided using two regression problems obtained from UCI.
关 键 词: 集成学习算法; 鲁棒性; 回归问题
课程来源: 视频讲座网
最后编审: 2019-04-27:lxf
阅读次数: 67