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Farago, A. and Lugosi, G. (n.d.) Strong Universal Consistency of Neural Network Classifiers. Proceedings. IEEE International Symposium on Information Theory, San Antonio, 17-22 January 1993, 431.
https://doi.org/10.1109/isit.1993.748747

has been cited by the following article:

• TITLE: The Role and Place of Artificial Neural Network Architectures Structural Redundancy in the Input Data Prototypes and Generalization Development

  AUTHORS: Conrad Onésime Oboulhas Tsahat, Ngoulou-A-Ndzeli, Béranger Destin Ossibi

  KEYWORDS: Multilayer Neural Network, Multidimensional Nonlinear Interpolation, Generalization by Similarity, Artificial Intelligence, Prototype Development

  JOURNAL NAME: Journal of Computer and Communications, Vol.12 No.7, July 17, 2024

  ABSTRACT: Neural Networks (NN) are the functional unit of Deep Learning and are known to mimic the behavior of the human brain to solve complex data-driven problems. Whenever we train our own neural networks, we need to take care of something called the generalization of the neural network. The performance of Artificial Neural Networks (ANN) mostly depends upon its generalization capability. In this paper, we propose an innovative approach to enhance the generalization capability of artificial neural networks (ANN) using structural redundancy. A novel perspective on handling input data prototypes and their impact on the development of generalization, which could improve to ANN architectures accuracy and reliability is described.