A study on effectiveness of extreme learning machine |
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Authors: | Yuguang WangAuthor VitaeFeilong CaoAuthor Vitae Yubo YuanAuthor Vitae |
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Affiliation: | Department of Mathematics, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China |
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Abstract: | Extreme learning machine (ELM), proposed by Huang et al., has been shown a promising learning algorithm for single-hidden layer feedforward neural networks (SLFNs). Nevertheless, because of the random choice of input weights and biases, the ELM algorithm sometimes makes the hidden layer output matrix H of SLFN not full column rank, which lowers the effectiveness of ELM. This paper discusses the effectiveness of ELM and proposes an improved algorithm called EELM that makes a proper selection of the input weights and bias before calculating the output weights, which ensures the full column rank of H in theory. This improves to some extend the learning rate (testing accuracy, prediction accuracy, learning time) and the robustness property of the networks. The experimental results based on both the benchmark function approximation and real-world problems including classification and regression applications show the good performances of EELM. |
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Keywords: | Feedforward neural networks Extreme learning machine Effective extreme learning machine |
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