International Conference on Intelligent and Advanced Systems 2007 Study on Extension-based Fuzzy Pattern Recognition

Pattern recognition, especially fuzzy pattern recognition, has been a study hotspot and important application of the artificial intelligence and machine learning. To solve the shortages of the existing fuzzy pattern recognition, this paper puts forward a new method, which is based on extension theory. It firstly takes advantage of the basal concepts, such as matter-element, dependent function in extension theory, to set up an extension-based fuzzy pattern recognition model, and then uses an emulational instance-the voice recognition to testify it. It is proved that this new method can not only conquer the shortages of the existing fuzzy pattern recognition methods, but also have more efficiency and applications.

[1]  Marcin Luckner.  Recognition of Noised Patterns Using Non-Disruption Learning Set , 2006, Sixth International Conference on Intelligent Systems Design and Applications.

[2]  Silvana G. Dellepiane,et al.  Learning optimal classifier through fuzzy recognition rate maximization , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[3]  Wen Cai.  Extension theory and its application , 1999 .

[4]  Anton A. Bougaev.  Pattern recognition based tools enabling autonomic computing. , 2005, Second International Conference on Autonomic Computing (ICAC'05).

[5]  Nicolás García-Pedrajas,et al.  Improving multiclass pattern recognition by the combination of two strategies , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  B. Shukhat,et al.  Fuzzy patterns recognition , 1995, Proceedings of 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society.