Abstract
Deep learning is a relatively new research area, motivated by the need to obtain more accurate, flexible and applicable methods for knowledge discovery. However, deep learning is a much wider concept, which entails a deep understanding of a scenario and its corresponding parameters, aiming to fully describe the interconnections between them. In this paper we will argue that a “gut-feeling” approach can be potentially utilised to obtain accurate results, and we will consider an initial approach to evaluate specific information captured by dependency networks.
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Johnny, O., Trovati, M., Ray, J. (2018). The “Gut-Feeling” in the Decision-Making Process: A Computationally Efficient Approach to Influence Relation Assessment. In: Barolli, L., Woungang, I., Hussain, O. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 8. Springer, Cham. https://doi.org/10.1007/978-3-319-65636-6_52
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