【新书推荐】【2019.09】2018年ELM会议论文集
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本书包含2018年11月21日至23日在新加坡举行的2018年极限学习机国际会议的部分论文。
This book contains some selected papers from the International Conference on Extreme Learning Machine 2018, which was held in Singapore, November 21–23, 2018.
本次会议为学术界、研究人员和工程师提供了一个交流和分享ELM技术、大脑学习理论研究和实际应用研发经验的论坛。
This conference provided a forum for academics, researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the ELM technique and brain learning.
极限学习机器（ELM）旨在实现普适学习和普适智能。
Extreme Learning Machines (ELM) aims to enable pervasive learning and pervasive intelligence.
正如ELM理论所倡导的那样，从长远的角度来看，机器学习和生物学习的融合是令人兴奋的。
As advocated by ELM theories, it is exciting to see the convergence of machine learning and biological learning from the long-term point of view.
ELM可能是填补机器学习和生物学习（其中的激活函数甚至未知）之间空白的基本“学习粒子”之一。
ELM may be one of the fundamental “learning particles” filling the gaps between machine learning and biological learning (of which activation functions are even unknown).
ELM代表了一套（机器和生物）学习技术，其中隐藏的神经元不需要调整：从它们的祖先遗传或随机产生。
ELM represents a suite of (machine and biological) learning techniques in which hidden neurons need not be tuned: inherited from their ancestors or randomly generated.
ELM学习理论表明，基于随机生成的隐层神经元（生物神经元、人工神经元、小波、傅立叶级数等）可以得到有效的学习算法，只要它们是非线性分段连续的，独立于训练数据和应用环境。
ELM learning theories show that effective learning algorithms can be derived based on randomly generated hidden neurons (biological neurons, artificial neurons, wavelets, Fourier series, etc.) as long as they are nonlinear piecewise continuous, independent of training data and application environments.
越来越多来自神经科学的证据表明类似的原理也适用于生物学习系统。
Increasingly, evidence from neuroscience suggests that similar principles apply in biological learning systems.
【【新书推荐】【2019.09】2018年ELM会议论文集】ELM理论和算法认为，“随机隐藏神经元”捕获了生物学习机制的一个重要方面以及一种直观感觉，即生物学习的效率不需要依赖于神经元的计算能力。
ELM theories and algorithms argue that “random hidden neurons” capture an essential aspect of biological learning mechanisms as well as the intuitive sense that the efficiency of biological learning need not rely on computing power of neurons.
因此，ELM理论暗示了大脑比现在的计算机更智能、更有效的可能原因。
ELM theories thus hint at possible reasons why the brain is more intelligent and effective than current computers.
ELM2018的主题是分层ELM、物联网AI、机器学习和生物学习的协同作用。
The main theme of ELM2018 is Hierarchical ELM, AI for IoT, Synergy of Machine Learning and Biological Learning.
这本书涵盖了ELM的理论、算法和应用，它使读者对ELM的最新进展一目了然。
This book covers theories, algorithms and applications of ELM. It gives readers a glance at the most recent advances of ELM.
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