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On optimization based extreme learning machine in primal for regression and classification by functional iterative method
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DOI：10.1007/s13042-014-0283-8.png)
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Abstract
En 中文
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.
Keywords:
Extreme learning machine
Single hidden layer feedforward neural networks
Functional iterative method
Support vector machine
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