Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement learning and Q-learning


Ji Su Park, Jong Hyuk Park, Journal of Information Processing Systems Vol. 16, No. 5, pp. 1001-1007, Oct. 2020  

10.3745/JIPS.02.0139
Keywords: Deep Learning, Machine Learning, Reinforcement Learning, Q-learning
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Abstract

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.


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Cite this article
[APA Style]
Ji Su Park and Jong Hyuk Park (2020). Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement learning and Q-learning. Journal of Information Processing Systems, 16(5), 1001-1007. DOI: 10.3745/JIPS.02.0139.

[IEEE Style]
J. S. Park and J. H. Park, "Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement learning and Q-learning," Journal of Information Processing Systems, vol. 16, no. 5, pp. 1001-1007, 2020. DOI: 10.3745/JIPS.02.0139.

[ACM Style]
Ji Su Park and Jong Hyuk Park. 2020. Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement learning and Q-learning. Journal of Information Processing Systems, 16, 5, (2020), 1001-1007. DOI: 10.3745/JIPS.02.0139.