Advances in Neural Networks -- ISNN 2010 (eBook, PDF)

7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I
Redaktion: Kwok, James; Lu, Bao-Liang; Zhang, Liqing; Lu, Bao-Liang

• Format: PDF

Produktbeschreibung

This book and its sister volume collect refereed papers presented at the 7th Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. Building on the success of the previous six successive ISNN symposiums, ISNN has become a well-established series of popular and high-quality conferences on neural computation and its applications. ISNN aims at providing a platform for scientists, researchers, engineers, as well as students to gather together to present and discuss the latest progresses in neural networks, and applications in diverse areas. Nowadays, the field of neural networks has been fostered far beyond the traditional artificial neural networks. This year, ISNN 2010 received 591 submissions from more than 40 countries and regions. Based on rigorous reviews, 170 papers were selected for publication in the proceedings. The papers collected in the proceedings cover a broad spectrum of fields, ranging from neurophysiological experiments, neural modeling to extensions and applications of neural networks. We have organized the papers into two volumes based on their topics. The first volume, entitled "Advances in Neural Networks- ISNN 2010, Part 1," covers the following topics: neurophysiological foundation, theory and models, learning and inference, neurodynamics. The second volume en- tled "Advance in Neural Networks ISNN 2010, Part 2" covers the following five topics: SVM and kernel methods, vision and image, data mining and text analysis, BCI and brain imaging, and applications.

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Produktdetails

• Verlag: Springer Berlin Heidelberg
• Seitenzahl: 757
• Erscheinungstermin: 30. Mai 2010
• Englisch
• ISBN-13: 9783642132780
• Artikelnr.: 44228779

Inhaltsangabe

Neurophysiological Foundation.- Theory and Models.- Learning and Inference.- Neurodynamics.