Extreme learning machine: RBF network case
Extreme learning machine: RBF network case
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DOI：
10.1109/icarcv.2004.1468985
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发表时间：
2004-12
期刊：
影响因子：
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通讯作者：
G. Huang;C. Siew
中科院分区：
文献类型：
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作者：
G. Huang;C. Siew
最近提出了一种新的学习算法，称为极端学习机（ELM）的单隐层前馈神经网络（SLFN），很容易实现良好的泛化性能，在极快的学习速度。ELM随机选择输入权重，并解析确定SLFN的输出权重。本文表明，ELM可以扩展到径向基函数（RBF）网络的情况下，它允许的中心和影响宽度的径向基函数（RBF）核是随机生成的，输出权重可以简单地解析计算，而不是迭代调整。有趣的是，实验结果表明，RBF网络的ELM算法可以以极快的速度完成学习，并在许多人工和真实的基准函数逼近和分类问题中产生非常接近SVM的泛化性能。由于ELM不需要对给定的网络架构进行验证和人为干预的参数，因此ELM可以很容易地使用。
A new learning algorithm called extreme learning machine (ELM) has recently been proposed for single-hidden layer feedforward neural networks (SLFNs) to easily achieve good generalization performance at extremely fast learning speed. ELM randomly chooses the input weights and analytically determines the output weights of SLFNs. This paper shows that ELM can be extended to radial basis function (RBF) network case, which allows the centers and impact widths of RBF kernels to be randomly generated and the output weights to be simply analytically calculated instead of iteratively tuned. Interestingly, the experimental results show that the ELM algorithm for RBF networks can complete learning at extremely fast speed and produce generalization performance very close to that of SVM in many artificial and real benchmarking function approximation and classification problems. Since ELM does not require validation and human-intervened parameters for given network architectures, ELM can be easily used.