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Theoretical Foundations of Deep Resonance Interference Network: Towards Intuitive Learning as a Wave Field Phenomenon
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Author(s): Christophe Thovex (LAFMIA (UMI 3175), France & Mexico)
Copyright: 2020
Pages: 23
Source title:
Security, Privacy, and Forensics Issues in Big Data
Source Author(s)/Editor(s): Ramesh C. Joshi (Graphic Era University, Dehradun, India)and Brij B. Gupta (National Institute of Technology, Kurukshetra, India)
DOI: 10.4018/978-1-5225-9742-1.ch015
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Abstract
Digital processes for banks, insurances, or public services generate big data. Hidden networks and weak signals from frauds activities are sometimes statistically undetectable in the endogenous data respective to processes. The organic intelligence of human experts is able to reverse-engineer new fraud scenarios without statistically significant characteristics, but machine learning usually needs to be taught about them or fails to this task. Deep resonance interference network is a multidisciplinary attempt in probabilistic machine learning inspired from waves temporal reversal in finite space, introduced for big data analysis and hidden data mining. It proposes a theoretical alternative to artificial neural networks for deep learning. It is presented along with experimental outcomes related to fraudulent processes generating data statistically similar to legal endogenous data. Results show particular findings probably due to the systemic nature of the model, which appears closer to reasoning and intuition processes than to the perception processes mainly simulated in deep learning.
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