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 publication and its sister volumes represent the complaints of the 3rd overseas Symposium on Neural Networks (ISNN 2006) held in Chengdu in southwestern China in the course of may perhaps 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 zone with turning out to be acceptance and bettering caliber. ISNN 2006 obtained 2472 submissions from authors in forty three nations 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 United States, South the United States, Africa, and Oceania). in keeping with rigorous reports, 616 high quality papers have been chosen for e-book within the lawsuits with the attractiveness price being below 25%. The papers are prepared in 27 cohesive sections masking all significant subject matters of neural community learn and improvement. as well as the various contributed papers, ten amazing 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 info for Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II
For extract feature extraction, 3 kinds of image are prepared: original gray images, edge images for emphasizing the outline of face, and combined images from two previous images. We compared the performance with respect to number of clusters in the first layer and the second layer changing vigilance parameter variously. 8 PF1 PF2 PF3 PF1 PF2 PF3 Fig. 2. Recognition accuracy and speed of H-ART2 According to searching strategies, the strategy of searching every candidate clusters(All), the strategy of searching only clusters in the first layer which have high similarity ranking with input pattern (Rank), and the proposed strategy of adaptively searching clusters in the first layer which have similarity ranking with input pattern by relative distance ratio between clusters (Max_Min) are experimented.
Moreover, we use the dynamical threshold learning algorithm to classify the remote sensing images, when the unbalanced level of classes is high, a good eﬀect is obtained. 1 Introduction Unbalanced training set refers to number unbalance of training patterns or probability distribution unbalance of diﬀerent groups . In this paper, we will stress the problem of number unbalance of training patterns, we call the group that has more training patterns the dominant group (large class), similarly, we call the group that has few training patterns the non-dominant group(small class).
Of course, in table 1, DTLA method achieves a high error on the large class, such as the mixture class, yet it should not be surprised because the improved performance on the small class is achieved at the cost of sacriﬁcing the performance on the large class. An other experiment is to extract the river from the satellite image using pixel classiﬁcation. The image, as shown in the ﬁgure 1(a), is a radar SAR remote sensing image, sized 298×291 pixels, photography in lower Mekong river in 1998, our aim is to detect the river correctly in this Classifying Unbalanced Pattern Groups by Training Neural Network 13 image.
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II by Do-Hyeon Kim, Eui-Young Cha, Kwang-Baek Kim (auth.), Jun Wang, Zhang Yi, Jacek M. Zurada, Bao-Liang Lu, Hujun Yin (eds.)