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Knowledge-based extreme learning machines

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Abstract

By incorporating prior knowledge in the form of implications into extreme learning machine (ELM), a novel knowledge-based extreme learning machine (KBELM) formulation is proposed in this work. In this approach, the nonlinear prior knowledge implications are converted into linear inequalities and are then included as linear equality constraints in the ELM formulation. The proposed KBELM formulation has the advantage that it leads to solving a system of linear equations. Effectiveness of the proposed approach is demonstrated on three synthetic and the publicly available Wisconsin Prognostic Breast Cancer datasets by comparing their results with ELM and optimally pruned ELM using additive and radial basis function hidden nodes.

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Acknowledgments

The authors are extremely thankful to the learned referees for their very helpful comments.

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Authors and Affiliations

  1. School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi, 110067, India

    S. Balasundaram & Deepak Gupta

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  1. S. Balasundaram
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  2. Deepak Gupta
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Correspondence to S. Balasundaram.

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Balasundaram, S., Gupta, D. Knowledge-based extreme learning machines. Neural Comput & Applic 27, 1629–1641 (2016). https://doi.org/10.1007/s00521-015-1961-5

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  • DOI: https://doi.org/10.1007/s00521-015-1961-5

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