Extreme learning machine: Theory and applications
Extreme learning machine: Theory and applications
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DOI：
10.1016/j.neucom.2005.12.126
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发表时间：
2006-12-01
期刊：
影响因子：
6
通讯作者：
Siew, Chee-Kheong
中科院分区：
文献类型：
--
作者：
Huang, Guang-Bin;Zhu, Qin-Yu;Siew, Chee-Kheong
很明显，前馈神经网络的学习速度通常远远低于要求，并且在过去几十年中一直是其应用的主要瓶颈。其背后的两个关键原因可能是：（1）基于梯度的慢速学习算法被广泛用于训练神经网络，以及（2）通过使用这种学习算法迭代地调整网络的所有参数。与这些传统的实现，本文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），随机选择隐藏节点，并解析确定SLFN的输出权重。从理论上讲，该算法往往提供良好的泛化性能在极快的学习速度。基于几个人工和真实的基准函数逼近和分类问题（包括非常大的复杂应用）的实验结果表明，新算法在大多数情况下都能产生良好的泛化性能，学习速度比传统的流行的前馈神经网络学习算法快数千倍。(I)（c）2006 Elsevier B.V.保留所有权利。
It is clear that the learning speed of feedforward neural networks is in general far slower than required and it has been a major bottleneck in their applications for past decades. Two key reasons behind may be: (1) the slow gradient-based learning algorithms are extensively used to train neural networks, and (2) all the parameters of the networks are tuned iteratively by using such learning algorithms. Unlike these conventional implementations, this paper proposes a new learning algorithm called extreme learning machine (ELM) for single-hidden layer feedforward neural networks (SLFNs) which randomly chooses hidden nodes and analytically determines the output weights of SLFNs. In theory, this algorithm tends to provide good generalization performance at extremely fast learning speed. The experimental results based on a few artificial and real benchmark function approximation and classification problems including very large complex applications show that the new algorithm can produce good generalization performance in most cases and can learn thousands of times faster than conventional popular learning algorithms for feedforward neural networks.(I) (c) 2006 Elsevier B.V. All rights reserved.