Handbook on Neural Information Processing

ebook Intelligent Systems Reference Library

By Monica Bianchini

cover image of Handbook on Neural Information Processing

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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
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    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.

    Handbook on Neural Information Processing