Ebook: Handbook on Neural Information Processing
- Tags: Computational Intelligence, Artificial Intelligence (incl. Robotics)
- Series: Intelligent Systems Reference Library 49
- Year: 2013
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
• 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
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.
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.