International Journal of Fuzzy Logic and Intelligent Systems

  • 제16권2호
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  • Pages.111-118
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  • 2016
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  • 1598-2645(pISSN)
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  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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Frequentist and Bayesian Learning Approaches to Artificial Intelligence

  • Jun, Sunghae (Department of Statistics, Cheongju University)
  • 투고 : 2016.04.30
  • 심사 : 2016.06.20
  • 발행 : 2016.06.30
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초록

Artificial intelligence (AI) is making computer systems intelligent to do right thing. The AI is used today in a variety of fields, such as journalism, medical, industry as well as entertainment. The impact of AI is becoming larger day after day. In general, the AI system has to lead the optimal decision under uncertainty. But it is difficult for the AI system can derive the best conclusion. In addition, we have a trouble to represent the intelligent capacity of AI in numeric values. Statistics has the ability to quantify the uncertainty by two approaches of frequentist and Bayesian. So in this paper, we propose a methodology of the connection between statistics and AI efficiently. We compute a fixed value for estimating the population parameter using the frequentist learning. Also we find a probability distribution to estimate the parameter of conceptual population using Bayesian learning. To show how our proposed research could be applied to practical domain, we collect the patent big data related to Apple company, and we make the AI more intelligent to understand Apple's technology.

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피인용 문헌

  1. Factor analysis and structural equation model for patent analysis: a case study of Apple’s technology vol.29, pp.7, 2017, https://doi.org/10.1080/09537325.2016.1227067