Handbook on Neural Information Processing

• Corporate Author: SpringerLink (Online service)
• Other Authors: Bianchini, Monica. (Editor), Maggini, Marco. (Editor), Jain, Lakhmi C. (Editor)
• Format: Electronic
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Series: Intelligent Systems Reference Library, 49
• Subjects: Engineering.; Artificial intelligence.; Computational Intelligence.; Artificial Intelligence (incl. Robotics).
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-36657-4

 This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include: ����������������������� Deep architectures ����������������������� Recurrent, recursive, and graph neural networks ����������������������� Cellular neural networks ����������������������� Bayesian networks ����������������������� Approximation capabilities of neural networks ����������������������� Semi-supervised learning ����������������������� Statistical relational learning ����������������������� Kernel methods for structured data ����������������������� Multiple classifier systems ����������������������� Self organisation and modal learning ����������������������� Applications to content-based image retrieval, text mining in large document collections, and bioinformatics � This book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.