Handbook on Neural Information Processing

✍ Scribed by Yoshua Bengio, Aaron Courville (auth.), Monica Bianchini, Marco Maggini, Lakhmi C. Jain (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2013
• Tongue: English
• Leaves: 546
• Series: Intelligent Systems Reference Library 49
• Edition: 1
• Category: Library

✦ Synopsis

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

✦ Table of Contents

Front Matter....Pages 1-18
Deep Learning of Representations....Pages 1-28
Recurrent Neural Networks....Pages 29-65
Supervised Neural Network Models for Processing Graphs....Pages 67-96
Topics on Cellular Neural Networks....Pages 97-141
Approximating Multivariable Functions by Feedforward Neural Nets....Pages 143-181
Bochner Integrals and Neural Networks....Pages 183-214
Semi-supervised Learning....Pages 215-239
Statistical Relational Learning....Pages 241-281
Kernel Methods for Structured Data....Pages 283-333
Multiple Classifier Systems: Theory, Applications and Tools....Pages 335-378
Self Organisation and Modal Learning: Algorithms and Applications....Pages 379-400
Bayesian Networks, Introduction and Practical Applications....Pages 401-431
Relevance Feedback in Content-Based Image Retrieval: A Survey....Pages 433-469
Learning Structural Representations of Text Documents in Large Document Collections....Pages 471-503
Neural Networks in Bioinformatics....Pages 505-525
Back Matter....Pages 527-537

✦ Subjects

Computational Intelligence; Artificial Intelligence (incl. Robotics)