给出了一种指导山脊极限学习机中山脊参数选择的系统方法

M. Er, Zhifei Shao, Ning Wang
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引用次数: 9

摘要

极限学习机(Extreme Learning Machine, ELM)由于其极快的学习速度和良好的泛化性能,作为一种通用函数逼近器受到了广泛的关注。近年来,在ELM中出现了将其与脊回归相结合的新趋势,其稳定性和泛化性能得到了提高。然而,这个岭参数是通过试错的方式确定的,对于自动学习应用来说,这是一种不令人满意的方法。本文讨论了脊状神经网络与普通神经网络的区别,以及脊状神经网络的特殊性质和各种推导脊状神经网络参数的方法。在此基础上,提出了一种基于脊线ELM特性的半交叉验证脊线参数选择方法。这种方法被称为半交叉验证岭ELM (SC-R-ELM),也被证明可以在11个回归数据集中获得稳健和可靠的结果。
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A systematic method to guide the choice of ridge parameter in ridge extreme learning machine
Extreme Learning Machine (ELM) has attracted many researchers as a universal function approximator because of its extremely fast learning speed and good generalization performance. Recently, a new trend in ELM emerges to combine it with ridge regression, which has been shown improved stability and generalization performance. However, this ridge parameter is determined through a trial-and-error manner, an unsatisfactory approach for automatic learning applications. In this paper, the differences between ridge ELM and ordinary Neural Networks are discussed as well as special properties of ridge ELM and various approaches to derive the ridge parameter. Furthermore, a semi-cross-validation ridge parameter selection procedure based on the special properties of ridge ELM is proposed. This approach, termed as Semi-Cross-validation Ridge ELM (SC-R-ELM), is also demonstrated to achieve robust and reliable results in 11 regression data sets.
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