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Link to original content: https://doi.org/10.1007/s11432-014-5269-3
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Extreme learning machines: new trends and applications

极限学习机: 新趋势与新应用

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  • Special Focus on High-Speed Signal Processing
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Abstract

Extreme learning machine (ELM), as a new learning framework, draws increasing attractions in the areas of large-scale computing, high-speed signal processing, artificial intelligence, and so on. ELM aims to break the barriers between the conventional artificial learning techniques and biological learning mechanism and represents a suite of machine learning techniques in which hidden neurons need not to be tuned. ELM theories and algorithms argue that “random hidden neurons” capture the essence of some brain learning mechanisms as well as the intuitive sense that the efficiency of brain learning need not rely on computing power of neurons. Thus, compared with traditional neural networks and support vector machine, ELM offers significant advantages such as fast learning speed, ease of implementation, and minimal human intervention. Due to its remarkable generalization performance and implementation efficiency, ELM has been applied in various applications. In this paper, we first provide an overview of newly derived ELM theories and approaches. On the other hand, with the ongoing development of multilayer feature representation, some new trends on ELM-based hierarchical learning are discussed. Moreover, we also present several interesting ELM applications to showcase the practical advances on this subject.

摘要

创新点

极限学习机作为一种全新的机器学习理论和框架, 在大数据计算、 高速信号处理, 人工智能等领域越来越受到关注。 极限学习机旨在打破传统学习理论和生物学习机制之间的壁垒, 该理论认为人脑的学习效率不依赖于单个神经元的计算能力, 因此极限学习机通过随机产生的隐层神经元来逼近大脑学习机理, 取得了比传统神经网络和支持向量机更高的学习精度、 更快的训练速度以及更少的人为干预。 本文对近年来提出的极限学习机新理论和新方法进行综述, 在此基础上重点介绍基于极限学习机的多层特征表征方面的最新研究成果, 最后介绍极限学习机的实际应用。

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Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    ChenWei Deng, Jia Xu & JieXiong Tang

  2. School of Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    GuangBin Huang

Authors
  1. ChenWei Deng
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  2. GuangBin Huang
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  3. Jia Xu
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  4. JieXiong Tang
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Correspondence to GuangBin Huang.

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Deng, C., Huang, G., Xu, J. et al. Extreme learning machines: new trends and applications. Sci. China Inf. Sci. 58, 1–16 (2015). https://doi.org/10.1007/s11432-014-5269-3

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