Ensemble learning via negative correlation
Ensemble learning via negative correlation
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DOI：
10.1016/s0893-6080(99)00073-8
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发表时间：
1999-12-01
期刊：
影响因子：
7.8
通讯作者：
Yao, X
中科院分区：
文献类型：
--
作者：
Liu, Y;Yao, X
本文提出了一种神经网络集成的学习方法，即负相关学习。与以前的神经网络集成学习方法不同，负相关学习试图在集成中训练单个网络，并将它们组合在同一个学习过程中。在负相关学习中，集合中的所有个体网络通过其误差函数中的相关惩罚项同时且交互地训练。负相关学习可以创建负相关网络以鼓励个体网络之间的专业化和合作，而不是产生误差不相关的无偏个体网络，已经进行了实证研究，以显示为什么和如何负相关学习的作品。实验结果表明，负相关学习可以产生具有良好泛化能力的神经网络集成。(C)1999 Elsevier Science Ltd.保留所有权利。
This paper presents a learning approach, i.e. negative correlation learning, for neural network ensembles. Unlike previous learning approaches for neural network ensembles, negative correlation learning attempts to train individual networks in an ensemble and combines them in the same learning process. in negative correlation learning, all the individual networks in the ensemble are trained simultaneously and interactively through the correlation penalty terms in their error functions, Rather than producing unbiased individual networks whose errors are uncorrelated, negative correlation learning can create negatively correlated networks to encourage specialisation and cooperation among the individual networks, Empirical studies have been carried out to show why and how negative correlation learning works. The experimental results show that negative correlation learning can produce neural network ensembles with good generalisation ability. (C) 1999 Elsevier Science Ltd. All rights reserved.