计算机科学 ›› 2009, Vol. 36 ›› Issue (9): 208-210.
• 人工智能 • 上一篇 下一篇
陈凯,马景义
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CHEN Kai,MA Jing-yi
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摘要: 集成学习已成为机器学习研究的一大热点。提出了一种综合Bagging和Boosting技术特点,以分类回归树为基学习器构造一种新的相似度指标用于聚类并利用聚类技术和贪婪算法进行选择性集成学习的算法—SER-BagBoosting Trees算法。算法主要应用于回归问题。实验表明,该算法往往比其它算法具有更好的泛化性能和更高的运行效率。
关键词: 分类回归树,自助法,选择性集成
Abstract: Ensemble learning now becomes much popular in the field of machine learning. "hhis paper introduced a new ensemble algorithm, SER-BagBoosting Trees ensemble algorithm, which was a combination of tree predictors and was based on variational similarity cluster technology and greedy method, and it was also combined with the features of Boosting and Bagging. Compared with a series of other learning algorithms, it often has better generalization ability and higher efficiency.
Key words: CART, Bootstrap, Selective ensemble
陈凯,马景义. 一种选择性SER-BagBoosting Trees集成学习研究[J]. 计算机科学, 2009, 36(9): 208-210. https://doi.org/
CHEN Kai,MA Jing-yi. Study of a Selective Ensemble Algorithm Named SER-BagBoosting Trees[J]. Computer Science, 2009, 36(9): 208-210. https://doi.org/
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