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Engineering - Computational Intelligence and Complexity | Handbook on Neural Information Processing

Handbook on Neural Information Processing

Bianchini, Monica, Maggini, Marco, Jain, Lakhmi C. (Eds.)

2013, XX, 538 p. 144 illus.

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  • Contains the latest research in the area of neural information systems and their applications
  • Written by leading experts
  • State-of-the-Art of the book

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.

Content Level » Research

Keywords » Computational Intelligence - Neural Information Processing

Related subjects » Artificial Intelligence - Computational Intelligence and Complexity

Table of contents 

Neural Network Architectures.- Learning paradigms.-

Reasoning and applications.- conclusions.

Reasoning and applications.- conclusions.

Reasoning and applications.- conclusions.

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