Deep Stochastic Configuration Networks

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DeepSCN

  Key References

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  2. Y. H. Pao and Y. Takefji, Functional-link net computing . IEEE Computer Journal, 25(5):76-79, 1992. [PDF]

  3. B. Igelnik and Y. H. Pao, Stochastic choice of basis functions in adaptive function approximation and the functional-link net . IEEE Transactions on Neural Networks, 6(6):1320-1329, 1995. [PDF]

  4. H. Jaeger, Adaptive nonlinear system identification with echo state networks . In Advances in Neural Information Processing Systems, pages 593-600, 2002. [PDF]

  5. A. Rahimi and B. Recht, Random features for large-scale kernel machines . In Advances in Neural Information Processing Systems, pages 1177-1184, 2007. [PDF]

  6. A. Rahimi and B. Recht, Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning . In Advances in Neural Information Processing Systems, pages 1313-1320, 2009. [PDF]

  7. A. N. Gorban, I. Y. Tyukin, D. V. Prokhorov, and K. I. Sofeikov, Approximation with random bases: Pro et contra . Information Sciences, 364:129-145, 2016. [PDF]

  8. D. Wang, Editorial: Randomized algorithms for training neural networks . Information Sciences, 364:126-128, 2016. [PDF]

  9. M. Li and D. Wang, Insights into randomized algorithms for neural networks: Practical issues and common pitfalls . Information Sciences, 382-383:170-178, 2016. [PDF]

  10. S. Scardapane and D. Wang, Randomness in neural networks: An overview . WIREs Data Mining and Knowledge Discovery, e1200.doi: 10.1002/widm.1200, 2017. [PDF]