New PDF release: Advances in Neural Networks - ISNN 2006: Third International

By Do-Hyeon Kim, Eui-Young Cha, Kwang-Baek Kim (auth.), Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin (eds.)

This ebook and its sister volumes represent the complaints of the 3rd foreign Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China in the course of might 28–31, 2006. After a profitable ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN grew to become a well-established sequence of meetings on neural computation within the area with growing to be reputation and bettering caliber. ISNN 2006 bought 2472 submissions from authors in forty three international locations and areas (mainland China, Hong Kong, Macao, Taiwan, South Korea, Japan, Singapore, Thailand, Malaysia, India, Pakistan, Iran, Qatar, Turkey, Greece, Romania, Lithuania, Slovakia, Poland, Finland, Norway, Sweden, Demark, Germany, France, Spain, Portugal, Belgium, Netherlands, united kingdom, eire, Canada, united states, Mexico, Cuba, Venezuela, Brazil, Chile, Australia, New Zealand, South Africa, Nigeria, and Tunisia) throughout six continents (Asia, Europe, North the USA, South the United States, Africa, and Oceania). in response to rigorous experiences, 616 high quality papers have been chosen for booklet within the complaints with the popularity price being under 25%. The papers are geared up in 27 cohesive sections overlaying all significant issues of neural community study and improvement. as well as the various contributed papers, ten special students gave plenary speeches (Robert J. Marks II, Erkki Oja, Marios M. Polycarpou, Donald C. Wunsch II, Zongben Xu, and Bo Zhang) and tutorials (Walter J. Freeman, Derong Liu, Paul J. Werbos, and Jacek M. Zurada).

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Extra resources for Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II

Example text

Unfortunately, CFNN is only suitable for the classification of numerical data and some non-numerical data that can be transferred to numerical data. However, complicated pattern usually includes both numerical data and non-numerical data, so CFNN is not suitable for classification of such complicated data. Now, let’s give a brief review of the preliminary concepts and notations of pattern classification [6]. The universe of objects can be regarded as 3-tuples as follows: {U, C, V}, where U, C and V are three finite, non-empty sets.

2 Feature Extraction Using 2-D Wavelet Transform To analyze the iris image using Haar wavelet, the image in the polar coordinate is first transformed into the image in 2-D Cartesian coordinate and then is decomposed multiple subbands by the pyramid decomposition algorithm. Since sharp transition in the 2-D iris pattern is well represented by each HH subband, we can use each pixel intensity values of a HH subband as the feature vector for the classification. Since HH1 subband has too many pixels and is sensitive to the error of the iris location, we use a coarser HH subband such as HH4 or HH5.

Compared with the other training schemes, the proposed method (DTLA) can improve the classification rate of NN on the small class. Experiments on classifying the satellite images show a good effect. Therefore, we think that although the data-set of the satellite image maybe be unbalanced, ambiguous and nonlinear, DTLA algorithm can make NN work well to detect the interesting objects. Acknowledgements The research work was supported by China State Major Basic Research Project (2001CB309401), the National Natural Science Foundation of China, project No.

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