Buch, Englisch, Band 3173, 1044 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1598 g
International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Proceedings, Part I
Buch, Englisch, Band 3173, 1044 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 1598 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-540-22841-7
Verlag: Springer
This book constitutes the proceedings of the International Symposium on Neural N- works (ISNN 2004) held in Dalian, Liaoning, China during August 19–21, 2004. ISNN 2004 received over 800 submissions from authors in ?ve continents (Asia, Europe, North America, South America, and Oceania), and 23 countries and regions (mainland China, Hong Kong, Taiwan, South Korea, Japan, Singapore, India, Iran, Israel, Turkey, H- gary, Poland, Germany, France, Belgium, Spain, UK, USA, Canada, Mexico, Venezuela, Chile, and Australia). Based on reviews, the Program Committee selected 329 hi- quality papers for presentation at ISNN 2004 and publication in the proceedings. The papers are organized into many topical sections under 11 major categories (theore- cal analysis; learning and optimization; support vector machines; blind source sepa- tion, independent component analysis, and principal component analysis; clustering and classi?cation; robotics and control; telecommunications; signal, image and time series processing; detection, diagnostics, and computer security; biomedical applications; and other applications) covering the whole spectrum of the recent neural network research and development. In addition to the numerous contributed papers, ?ve distinguished scholars were invited to give plenary speeches at ISNN 2004. ISNN 2004 was an inaugural event. It brought together a few hundred researchers, educators, scientists, and practitioners to the beautiful coastal city Dalian in northeastern China.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Theoretical Analysis.- Learning and Optimization.- Support Vector Machines.- Blind Source Separation, Independent Component Analysis and Principal Component Analysis.- Clustering and Classification.
