Neural Information Processing 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I /

• Corporate Author: SpringerLink (Online service)
• Other Authors: Ishikawa, Masumi (Editor), Doya, Kenji (Editor), Miyamoto, Hiroyuki (Editor), Yamakawa, Takeshi (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.
• Series: Lecture Notes in Computer Science, 4984
• Subjects: Computer science.; Computers.; Database management.; Artificial intelligence.; Image processing.; Pattern recognition.; Computer Science.; Artificial Intelligence (incl. Robotics).; Computation by Abstract Devices.; Pattern Recognition.; Information Systems Applications (incl. Internet).; Database Management.; Image Processing and Computer Vision.

 The two volume set LNCS 4984 and LNCS 4985 constitutes the thoroughly refereed post-conference proceedings of the 14th International Conference on Neural Information Processing, ICONIP 2007, held in Kitakyushu, Japan, in November 2007, jointly with BRAINIT 2007, the 4th International Conference on Brain-Inspired Information Technology. The 228 revised full papers presented were carefully reviewed and selected from numerous ordinary paper submissions and 15 special organized sessions. The 116 papers of the first volume are organized in topical sections on computational neuroscience, learning and memory, neural network models, supervised/unsupervised/reinforcement learning, statistical learning algorithms, optimization algorithms, novel algorithms, as well as motor control and vision. The second volume contains 112 contributions related to statistical and pattern recognition algorithms, neuromorphic hardware and implementations, robotics, data mining and knowledge discovery, real world applications, cognitive and hybrid intelligent systems, bioinformatics, neuroinformatics, brain-conputer interfaces, and novel approaches. 

 Computational Neuroscience -- Learning and Memory -- Neural Network Models -- Supervised/Unsupervised/Reinforcement Learning -- Statistical Learning Algorithms -- Optimization Algorithms -- Novel Algorithms -- Motor Control and Vision.