Advanced Intelligent Computing Theories and Applications: Third International Conference on Intelligent Computing, ICIC 2007, Qingdao, China, August 21-24, 2007, ProceedingsDe-Shuang Huang, Laurent Heutte, Marco Loog The International Conference on Intelligent Computing (ICIC) was formed to provide an annual forum dedicated to the emerging and challenging topics in artificial intelligence, machine learning, bioinformatics, and computational biology, etc. It aims to bring - gether researchers and practitioners from both academia and industry to share ideas, problems and solutions related to the multifaceted aspects of intelligent computing. ICIC 2007, held in Qingdao, China, August 21-24, 2007, constituted the Third - ternational Conference on Intelligent Computing. It built upon the success of ICIC 2006 and ICIC 2005 held in Kunming and Hefei, China, 2006 and 2005, respectively. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was “Advanced Intelligent Computing Technology and Applications”. Papers focusing on this theme were solicited, addressing theories, methodologies, and applications in science and technology. |
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Common terms and phrases
analysis application approach Berlin Heidelberg 2007 calculated cell Chen China classification clustering coefficient complexity component Computer concept concept lattice constraints convergence Cosine Similarity crossover data mining data set decision defined delays denotes diagnosis distribution dynamic eigenvalues equation Exponential Stability function fuzzy number Fuzzy Sets genetic algorithm Heutte Huang humanoid robot ICIC IEEE IEEE Trans initial input iteration kernel layer learning linear LNAI Loog Eds Machine matrix method minimal mutation neural network neurons node nonlinear objective obtained operation optimal solution optimization problems output paper parameters Particle Swarm Optimization performance proposed QPSO rough set samples scheduling Science selection similarity simulation solving space Springer-Verlag Berlin Heidelberg Step structure Support Vector Machines swarm intelligence Table Theorem update User value propagation variables Wang wavelet weight Zhang