[논문]Frequentist and Bayesian Learning Approaches to Artificial Intelligence

Jun, Sunghae

doi:10.5391/ijfis.2016.16.2.111

Frequentist and Bayesian Learning Approaches to Artificial Intelligence 원문보기

International journal of fuzzy logic and intelligent systems : IJFIS, v.16 no.2, 2016년, pp.111 - 118

Jun, Sunghae (Department of Statistics, Cheongju University)

Abstract ▼ AI-Helper

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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문제 정의

Statistics and machine learning are typical approaches to AI learning [5]. In this paper, we focus on the statistics for making AI more intelligent. Ross [6] defined statistics as follow; “Statistics is the art of learning from data.
The error in statistics controls the uncertainty of complex environments by probabilistic approach. In this paper, we propose a connection between statistics and AI to improve the performance of AI technology. The remainder of this paper is made up as follows.

가설 설정

In this paper, we proposed frequentist and Bayesian approaches to make computer intelligent. Both frequentist and Bayesian are statistical approaches to learning from data.
In addition, most of them were based on the learning methodologies such as fuzzy logic or statistics [10- 13]. In this paper, we study on the learning methods based on statistics for making AI system. Statistics has two approaches, frequentist and Bayesian to data analysis [14].

참고문헌 (21)

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상세보기
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원문보기 상세보기
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원문보기 상세보기
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원문보기 상세보기
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원문보기 상세보기
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상세보기
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상세보기
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Frequentist and Bayesian Learning Approaches to Artificial Intelligence 원문보기

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