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Titlebook: Advances in Neural Networks - ISNN 2006; Third International Jun Wang,Zhang Yi,Hujun Yin Conference proceedings 2006 Springer-Verlag Berli

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期刊全称Advances in Neural Networks - ISNN 2006
期刊简称Third International
影响因子2023Jun Wang,Zhang Yi,Hujun Yin
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Advances in Neural Networks - ISNN 2006; Third International  Jun Wang,Zhang Yi,Hujun Yin Conference proceedings 2006 Springer-Verlag Berli
影响因子This book and its sister volumes constitute the Proceedings of the Third International Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China during May 28–31, 2006. After a successful ISNN 2004 in Dalian and ISNN 2005 in Chongqing, ISNN became a well-established series of conferences on neural computation in the region with growing popularity and improving quality. ISNN 2006 received 2472 submissions from authors in 43 countries and regions (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, UK, Ireland, Canada, USA, Mexico, Cuba, Venezuela, Brazil, Chile, Australia, New Zealand, South Africa, Nigeria, and Tunisia) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Based on rigorous reviews, 616 high-quality papers were selected for publication in the proceedings with the acceptance rate being less than 25%. The papers are organized in 27 cohesive sections covering all major topics of neural network research
Pindex Conference proceedings 2006
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书目名称Advances in Neural Networks - ISNN 2006影响因子(影响力)




书目名称Advances in Neural Networks - ISNN 2006影响因子(影响力)学科排名




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书目名称Advances in Neural Networks - ISNN 2006网络公开度学科排名




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书目名称Advances in Neural Networks - ISNN 2006被引频次学科排名




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书目名称Advances in Neural Networks - ISNN 2006年度引用学科排名




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书目名称Advances in Neural Networks - ISNN 2006读者反馈学科排名




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https://doi.org/10.1007/978-1-4614-5803-6ecognition are extracted from the segmented iris pattern using two-dimensional (2-D) wavelet transform based on Haar wavelet. We present an efficient initialization method of the weight vectors and a new method to determine the winner in LVQ neural network. The proposed methods have more accuracy than the conventional techniques.
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Zhao Zhang,Baoju Zhang,Hongwei Liu,Bo Zhangtes the capacities of biological brains for signal processing in pattern recognition. Its accuracy and efficiency are demonstrated in this report on an application to human face recognition, with comparisons of performance with conventional pattern recognition algorithms.
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/149143.jpg
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Lecture Notes in Electrical Engineering. First, a RGB image inputted from a frame grabber is converted into a HSV image. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction proc
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