Quaternion Neural Networks: state-of-the-Art and Research Challenges
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David García-Retuerta [1] ; Roberto Casado-Vara [1] ; Angel Martin-del Rey [1] [1] ; Prieta, Fernando de la [1] ; Javier Prieto [1] ; Corchado, Juan M. [1] [2]
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[1] Universidad de Salamanca
Universidad de Salamanca
Salamanca, España
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[2] AIR Institute
AIR Institute
Carbajosa de la Sagrada, España
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- Localización: Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings / Cesar Analide (ed. lit.), Paulo Novais (ed. lit.)
, David Camacho Fernández (ed. lit.) , Hujun Yin (ed. lit.), Vol. 2, 2020 (Part II), ISBN 978-3-030-62365-4, págs. 456-467 - Idioma: inglés
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Resumen
Machine Learning has recently emerged as a new paradigm for processing all types of information. In particular, Artificial Intelligence is attractive to corporations & research institutions as it provides innovative solutions for unsolved problems, & it enjoys a great popularity among the general public. However, despite the fact that Machine Learning offers huge opportunities for the IT industry, Artificial Intelligence technology is still at its infancy, with many issues to be addressed. In this paper, we present a survey of quaternion applications in Neural Networks, one of the most promising research lines in artificial vision which also has a great potential in several other topics. The aim of this paper is to provide a better understanding of the design challenges of Quaternion Neural Networks & identify important research directions in this increasingly important area.
