Download PDF by Do-Hyeon Kim, Eui-Young Cha, Kwang-Baek Kim (auth.), Jun: 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.)

ISBN-10: 3540344373

ISBN-13: 9783540344377

This publication 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 may possibly 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 transforming into attractiveness and enhancing 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 United States, South the United States, Africa, and Oceania). according to rigorous reports, 616 high quality papers have been chosen for e-book within the complaints with the popularity cost being under 25%. The papers are geared up in 27 cohesive sections overlaying all significant themes of neural community study and improvement. as well as the various contributed papers, ten extraordinary 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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Additional info for Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part II

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Selecting candidate clusters by cluster selection ratio, not simple similarity with input pattern, can decrease the risk of misclassification. 4 Experimental Result In this paper, facial data is acquired from the 320 x 240 x 24bit motion data of CCD camera. The number of people is 10, sex ratio is 6:4, and the ratio of people wearing glasses is 7:3. 1479 facial images of total 2215 are used to the training and the rest 736 is put to the test. 1 Performance Evaluation of the Proposed Pattern Classification Model Facial data are normalized with 25 x 25 and are enhanced with illumination plane and equalization.

CORE(A)= ∩ RED(A), where RED(A) is the set of all reductions of A. From definition 2, a set of the attribute A can be reduced to core(A). Classification by RS depends on equivalence relation. In fact, an attribute is an equivalence relation of U. Now, we can classify U according to core(A). , Pi S , ∪{ r can be reduced , if e Definition 3. A decision system (U C d}), B ⊆ C is a subset of conditional attributes, IND(B, {d})= (x,y) ∈ U×U |d(x) = d(y) or a ∈ B a(x)=a(y)} called binary relation or indiscernibility relation.

Fingerprint, hand, and face) vary with age, the structure of iris is stabilized after two or three year old and is unique to an individual [2]. Due to high distinctiveness and stability, the iris pattern has been considered as the most reliable biometric measurement for the identification of individuals. Compared with the fingerprint recognition, the iris recognition does not require physical contact with a sensing device and is not invasive. However, the photo image of the iris pattern taken at a distance can have various artifacts due to long eyelashes and eyelid on the iris, reflection of illumination, and variation of the pupil size in different illumination.

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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.)


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