Extreme learning machine: algorithm, theory and applications
Extreme learning machine: algorithm, theory and applications
复制标题
极限学习机：算法、理论与应用
DOI：
10.1007/s10462-013-9405-z
复制
发表时间：
2015-06-01
影响因子：
12
通讯作者：
Nie, Ru
中科院分区：
文献类型：
--
作者：
Ding, Shifei;Zhao, Han;Nie, Ru
极限学习机（ELM）是一种新的单隐层前向神经网络学习算法。与传统的神经网络学习算法相比，它克服了训练速度慢和过拟合问题。ELM基于经验风险最小化理论，其学习过程只需一次迭代。该算法避免了多次迭代和局部极小化。它具有较好的泛化能力、鲁棒性、可控性和较快的学习速度，已被广泛应用于各个领域和应用。本文从算法、理论和应用三个方面综述了ELM的最新研究进展。首先分析了ELM的理论和算法思想，然后跟踪描述了ELM近年来的最新进展，包括ELM的模型和具体应用，最后指出了ELM的未来研究和发展前景。
Extreme learning machine (ELM) is a new learning algorithm for the single hidden layer feedforward neural networks. Compared with the conventional neural network learning algorithm it overcomes the slow training speed and over-fitting problems. ELM is based on empirical risk minimization theory and its learning process needs only a single iteration. The algorithm avoids multiple iterations and local minimization. It has been used in various fields and applications because of better generalization ability, robustness, and controllability and fast learning rate. In this paper, we make a review of ELM latest research progress about the algorithms, theory and applications. It first analyzes the theory and the algorithm ideas of ELM, then tracking describes the latest progress of ELM in recent years, including the model and specific applications of ELM, finally points out the research and development prospects of ELM in the future.