DocumentCode :
2420812
Title :
Bayesian Interpretation of Adaptive Fuzzy Neural Network Model
Author :
Lee, Sang Wan ; Kim, Dae-Jin ; Kim, Yong Soo ; Bien, Zeungnam
Author_Institution :
Korean Adv. Inst. of Sci. & Technol., Daejeon
fYear :
0
fDate :
0-0 0
Firstpage :
2178
Lastpage :
2183
Abstract :
This paper conveys Bayesian interpretation of improved integrated adaptive fuzzy clustering(IAFC), which is one of the adaptive fuzzy neural network models and suggests upper bound of vigilance parameter, which gives us a guideline to endow IAFC with flexibility within the framework of minimum risk classifier. Besides, we proposed the off-line and on-line learning strategy of IAFC. The proposed techniques are applied to construct facial expression recognition system dealing with neutral, happy, sad, and angry. We empirically show that proposed methods are able to outperform the conventional IAFC.
Keywords :
Bayes methods; decision theory; emotion recognition; face recognition; fuzzy neural nets; fuzzy set theory; image classification; learning (artificial intelligence); pattern clustering; Bayesian decision theory; adaptive fuzzy neural network model; facial expression recognition system; integrated adaptive fuzzy clustering; minimum risk classifier framework; offline learning strategy; online learning strategy; Adaptive systems; Bayesian methods; Clustering algorithms; Decision theory; Face recognition; Fuzzy neural networks; Guidelines; Intelligent robots; Partitioning algorithms; Service robots;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9488-7
Type :
conf
DOI :
10.1109/FUZZY.2006.1682002
Filename :
1682002
Link To Document :
بازگشت