80 citations to 10.1016/j.jmaa.2014.03.092 (Crossref Cited-By Service)
  1. Smaranda Belciug, “Parallel versus cascaded logistic regression trained single-hidden feedforward neural network for medical data”, Expert Systems with Applications, 170, 2021, 114538  crossref
  2. Zarita Zainuddin, Saeed Panahian Fard, 2014 10th International Conference on Natural Computation (ICNC), 2014, 72  crossref
  3. Marakhimov Avazjon Rakhimovich, Khudaybergenov Kabul Kadirbergenovich, 1323, 11th World Conference “Intelligent System for Industrial Automation” (WCIS-2020), 2021, 47  crossref
  4. Tujitan Chakraborty, “Strong Universal Consistency and Rate of Convergence of Fast Trained Deep Feedforward Networks”, SSRN Journal, 2019  crossref
  5. Danilo Costarelli, Gianluca Vinti, “Convergence for a family of neural network operators in Orlicz spaces”, Mathematische Nachrichten, 290, № 2-3, 2017, 226  crossref
  6. Zhixiang Chen, Feilong Cao, “Construction of feedforward neural networks with simple architectures and approximation abilities”, Math Methods in App Sciences, 44, № 2, 2021, 1788  crossref
  7. Sule Birim, Ipek Kazancoglu, Sachin Kumar Mangla, Aysun Kahraman, Yigit Kazancoglu, “The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods”, Ann Oper Res, 2022  crossref
  8. Charles K. Chui, Shao-Bo Lin, Ding-Xuan Zhou, “Deep Net Tree Structure for Balance of Capacity and Approximation Ability”, Front. Appl. Math. Stat., 5, 2019, 46  crossref
  9. Maxim Secor, Alexander V. Soudackov, Sharon Hammes-Schiffer, “Artificial Neural Networks as Propagators in Quantum Dynamics”, J. Phys. Chem. Lett., 12, № 43, 2021, 10654  crossref
  10. Danilo Costarelli, Gianluca Vinti, “Saturation Classes for Max-Product Neural Network Operators Activated by Sigmoidal Functions”, Results Math, 72, № 3, 2017, 1555  crossref
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