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Frequentist and Bayesian Learning Approaches to Artificial Intelligence
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 Title & Authors
Frequentist and Bayesian Learning Approaches to Artificial Intelligence
Jun, Sunghae;
  PDF(new window)
 Abstract
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.
 Keywords
Statistics;Artificial intelligence;Bayesian inference;Frequentist;Learning from data;Apple technology;
 Language
English
 Cited by
1.
Factor analysis and structural equation model for patent analysis: a case study of Apple’s technology, Technology Analysis & Strategic Management, 2016, 1  crossref(new windwow)
 References
1.
Google DeepMind, "AlphaGo: the first computer program to ever beat a professional player at the game of go," Available https://deepmind.com/alpha-go.html

2.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. van den Driessche, et al., "Mastering the game of Go with deep neural networks and tree search," Nature, vol. 529, no. 7587, pp. 484-489, 2016. http://dx.doi.org/10.1038/nature16961 crossref(new window)

3.
IBM Watson, Available http://www.ibm.com/smarterplanet/us/en/ibmwatson

4.
J. Jackson, "IBM Watson vanquishes human jeopardy foes," Available http://www.pcworld.com/article/219893

5.
S. J. Russell and P. Norvig, Artificial intelligence: a modern approach, 3rd ed. Essex: Pearson, 2014.

6.
S. M. Ross, Introduction to Probability and Statistics for Engineers and Scientists, 4th ed. (Transl. K. S. Lee). New York: Academic Press, 2012.

7.
J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Waltham, MA: Morgan Kaufmann, 2012.

8.
M. Berthold and D. J. Hand, Intelligent Data Analysis: An Introduction, Berlin: Springer, 1999. http://dx.doi.org/10.1007/978-3-662-03969-4

9.
M. Akritas, Probability and Statistics with R for Engineers and Scientists, Boston, MA: Pearson, 2015.

10.
A. B. Khalifa and H. Frigui, "Multiple instance Mamdani fuzzy inference," International Journal of Fuzzy Logic and Intelligent Systems, vol. 15, no. 4, pp. 217-231, 2015. http://dx.doi.org/10.5391/IJFIS.2015.15.4.217 crossref(new window)

11.
J. S. Kim and J. S. Jeong, "Pattern recognition of ship navigational data using support vector machine," International Journal of Fuzzy Logic and Intelligent Systems, vol. 15, no. 4, pp. 268-276, 2015. http://dx.doi.org/10.5391/IJFIS.2015.15.4.268 crossref(new window)

12.
M. Kim, "Online selective-sample learning of hidden Markov models for sequence classification," International Journal of Fuzzy Logic and Intelligent Systems, vol. 15, no. 3, pp. 145-152, 2015. http://dx.doi.org/10.5391/IJFIS.2015.15.3.145 crossref(new window)

13.
R. Zhao, D. W. Lee, and H. K. Lee, "Fuzzy logic based navigation for multiple mobile robots in indoor environments," International Journal of Fuzzy Logic and Intelligent Systems, vol. 15, no. 4, pp. 305-314, 2015. http://dx.doi.org/10.5391/IJFIS.2015.15.4.305 crossref(new window)

14.
S. B. McGrayne, The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines & Emerged Triumphant from two Centuries of Controversy, New Haven, CT: Yale University Press, 2011.

15.
A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin, Bayesian Data Analysis, 3rd ed. Boca Raton, FL: CRC Press, 2013.

16.
J. K. Kruschke, Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, 2nd ed. London: Elsevier, 2015.

17.
K. B. Korb and A. E. Nicholson, Bayesian Artificial Intelligence, 2nd ed. Boca Raton, FL: CRC Press, 2011. http://dx.doi.org/10.1201/b10391

18.
S. Jun and S. S. Park, "Examining technological innovation of apple using patent analysis," Industrial Management & Data Systems, vol. 113, no. 6, pp. 890-907, 2013. http://dx.doi.org/10.1108/IMDS-01-2013-0032 crossref(new window)

19.
J. M. Kim and S. Jun, "Graphical causal inference and copula regression model for apple keywords by text mining," Advanced Engineering Informatics, vol. 29, no. 4, pp. 918-929, 2015. http://dx.doi.org/10.1016/j.aei.2015.10.001 crossref(new window)

20.
I. Feinerer, K. Hornik, and D. Meyer, "Text mining infrastructure in R," Journal of Statistical Software, vol. 25, no. 5, pp. 1-54, 2008. http://dx.doi.org/10.18637/jss.v025.i05

21.
I. Feinerer and K. Hornik, "Package 'tm': Text Mining Package ver. 0.6-2," Available https://cran.r-project.org/web/packages/tm/tm.pdf