On optimization based extreme learning machine in primal for regression and classification by functional iterative method

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• 作者：S. Balasundaram ; Deepak Gupta
• 关键词：Extreme learning machine ; Single hidden layer feedforward neural networks ; Functional iterative method ; Support vector machine
• 刊名：International Journal of Machine Learning and Cybernetics
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：7
• 期：5
• 页码：707-728
• 全文大小：3,842 KB
• 刊物类别：Engineering
• 刊物主题：Artificial Intelligence and Robotics
  Statistical Physics, Dynamical Systems and Complexity
  Computational Intelligence
  Control , Robotics, Mechatronics
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1868-808X
• 卷排序：7

文摘

In this paper, the recently proposed extreme learning machine in the aspect of optimization method by Huang et al. (Neurocomputing, 74: 155–163, 2010) has been considered in its primal form whose solution is obtained by solving an absolute value equation problem by a simple, functional iterative algorithm. It has been proved under sufficient conditions that the algorithm converges linearly. The pseudo codes of the algorithm for regression and classification are given and they can be easily implemented in MATLAB. Experiments were performed on a number of real-world datasets using additive and radial basis function hidden nodes. Similar or better generalization performance of the proposed method in comparison to support vector machine (SVM), extreme learning machine (ELM), optimally pruned extreme learning machine (OP-ELM) and optimization based extreme learning machine (OB-ELM) methods with faster learning speed than SVM and OB-ELM demonstrates its effectiveness and usefulness.