Browse > Article
http://dx.doi.org/10.5391/JKIIS.2007.17.2.244

Personalized Media Control Method using Probabilistic Fuzzy Rule-based Learning  

Lee, Hyong-Euk (한국과학기술원 전자전산학과)
Kim, Yong-Hwi (한국과학기술원 전자전산학과)
Lee, Tae-Youb (한국과학기술원 전자전산학과)
Park, Kwang-Hyun (한국과학기술원 전자전산학과)
Kim, Yong-Soo (대전대학교 컴퓨터공학과)
Cho, Joon-Myun (한국전자통신연구원 지능형로봇연구단)
Bien, Z. Zenn (한국과학기술원 전자전산학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.17, no.2, 2007 , pp. 244-251 More about this Journal
Abstract
Intention reading technique is essential to provide personalized services toward more convenient and human-friendly services in complex ubiquitous environment such as a smart home. If a system has knowledge about an user's intention of his/her behavioral pattern, the system can provide mote qualified and satisfactory services automatically in advance to the user's explicit command. In this sense, learning capability is considered as a key function for the intention reading technique in view of knowledge discovery. In this paper, ore introduce a personalized media control method for a possible application iii a smart home. Note that data pattern such as human behavior contains lots of inconsistent data due to limitation of feature extraction and insufficiently available features, where separable data groups are intermingled with inseparable data groups. To deal with such a data pattern, we introduce an effective engineering approach with the combination of fuzzy logic and probabilistic reasoning. The proposed learning system, which is based on IFCS (Iterative Fuzzy Clustering with Supervision) algorithm, extract probabilistic fuzzy rules effectively from the given numerical training data pattern. Furthermore, an extended architectural design methodology of the learning system incorporating with the IFCS algorithm are introduced. Finally, experimental results of the media contents recommendation system are given to show the effectiveness of the proposed system.
Keywords
확률적 퍼지 논리;학습;지식 발견;유비쿼터스 환경;행동 패턴;미디어 제어;
Citations & Related Records
연도 인용수 순위
  • Reference
1 A. M. Meystel and J. S. Albus, Intelligent Systems: Architecture, Design, and Control, New York: John Wiley & Sons, Inc., 2002
2 H.A.Simon, 'Why should machine learn?' in Machine Learning, Springer-Verlag, pp.25-38, 1984
3 Z. Bien, H. Hee, S. Lee, and K. Park, 'Learning Techniques in Service Robotic Environments', Applied Artificial Intelligence: Proc. of 7th International FLINS Conference, Genoa, Italy, 2006
4 Nefti, S., Oussalah, M., 'Probabilistic fuzzy clustering algorithm', 2004 IEEE International Conference on Systems, Man and Cybernetics, Vol. 5, pp. 4786 - 4791, 10-13 Oct. 2004
5 Hyong -Euk Lee and Z. Zenn Bien, 'Design of a Probabilistic Fuzzy Rule-based Learning System for Effective Intention Reading in Human-Machine Interaction', Proc. of 3rd International Conference on Ubiquitous Robots and Ambient Intelligence, Korea, Oct, 2006
6 W. James, Principles of Psychology, New York: Holt, 1890
7 J.D.E. Gabrieli, 'Cognitive neuroscience of human memory', in Annu. Rev. Psychol., vol.49, pp.87-115, 1998   DOI   ScienceOn
8 J C. Bezdek, Pattern Recognition with Fuzzy Objective Algorithms, New York: Plenum, 1981
9 Hyong-Euk Lee, Kwang-Hvun Park, and Z. Zenn Bien, 'Iterative Fuzzy Clustering Algorithm with Supervision to Construct Probabilistic Fuzzy Rule Base from Numerical Data', IEEE Trans. Fuzzy Systems, accepted, 2007
10 A. Baddeley, Working Memory, Oxford Psychology Series, Oxford: Clarendon Press,1986
11 L. X. Wang and J. M. Mendel, 'Generating Fuzzy Rules by Learning from Examples', IEEE Trans. Systems, Man, and Cybernetics, vol. 22, no.6, pp. 1414-1427, 1992   DOI   ScienceOn
12 Hyong-Euk Lee, Yong -Hwi Kim, Kwang-Hyun Park, Yong-Soo Kim, .Iin-Woo jung, joonmyun Cho, MinGyoung Kim and Z. Zenn Bien, 'Fuzzy Inductive Learning System for Learning Preference of the User's Behavior Pattern', Journal of Korea Fuzzy Logic and Intelligent Systems Society, Vol. 15, No.7, pp. 804-812, 2005
13 Yingxu Wang, Ying Wang, 'Cognitive informatics models of the brain', IEEE Transactions on Systems, Man and Cybernetics, Part C, Vol. 36, Issue 2, pp. 203-207, March 2006   DOI   ScienceOn
14 X. L. Xie and G. Beni, 'Validity Measure for Fuzzy Clustering'. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 13, No.8, pp. 841-847, 1991   DOI   ScienceOn
15 Milan Sonika, 'Image Processing, Analysis and Machine Vision', PWS Publishing, pp. 83-87, 1999
16 Z. Zenn Bien and Hyong-Euk Lee, 'Effective Learning System Techniques for Human-Robot Interaction in Service Environment', Knowledge- Based Systems, accepted, 2007
17 Fred H. Hamker, 'Life-long learning Cell Structures-continuously learning without catastrophic interference', IEEE Trans. Neural Networks, Vol. 14, 2001
18 S. Grossberg, 'Nonlinear neural networks: principles, mechanisms and architectures', Neural Networks, vol. 1, no. 1, pp. 17-61, 1988
19 Z. Zenn Bien and Dimitar Stefanov(eds), Advances in Rehabilitation Robotics: Human friendly Techniques on Movement Assistance and Restoration for People with Disability, Springer Verlag, June 2004
20 Liu. Z. and Li. H. - X, 'Probabilistic fuzzy logic system for modeling and control', IEEE Transactions on Fuzzy Systems, vol. 13, Issue 6, pp. 848-859, Dec. 2005   DOI   ScienceOn