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{ "item_title" : "Handbook on Neural Information Processing", "item_author" : [" Monica Bianchini", "Marco Maggini", "Lakhmi C. Jain "], "item_description" : "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 learningKernel methods for structured dataMultiple classifier systemsSelf organisation and modal learningApplications to content-based image retrieval, text mining in large document collections, and bioinformaticsThis book is thought particularly for graduate students, researchers and practitioners, willing to deepen their knowledge on more advanced connectionist models and related learning paradigms.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/64/236/656/3642366562_b.jpg", "price_data" : { "retail_price" : "169.99", "online_price" : "169.99", "our_price" : "169.99", "club_price" : "169.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Handbook on Neural Information Processing|Monica Bianchini

Handbook on Neural Information Processing

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Overview

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.

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Details

  • ISBN-13: 9783642366567
  • ISBN-10: 3642366562
  • Publisher: Springer
  • Publish Date: April 2013
  • Dimensions: 9.21 x 6.14 x 1.19 inches
  • Shipping Weight: 2.1 pounds
  • Page Count: 538

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