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Handbook on Neural Information Processing

ISRL: HANDBOOK ON NEURAL INFORMATION PROCESSING

Information Technology

Computing & Information Technology

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B2B GT

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B2B & B2C

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.

POST SECONDARY FOUR YEAR

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Front Cover.
Editorial Board.
Title Page.
Copyright Page.
Foreword.
Preface.
Contents.
1: Deep Learning of Representations.
2: Recurrent Neural Networks.
3: Supervised Neural Network Models for Processing Graphs.
4: Topics on Cellular Neural Networks.
5: Approximating Multivariable Functions by Feedforward Neural Nets.
6: Bochner Integrals and Neural Networks.
7: Semi-Supervised Learning.
8: Statistical Relational Learning.
9: Kernel Methods for Structured Data.
10: Multiple Classifier Systems: Theory, Applications and Tools.
11: Self Organisation and Modal Learning: Algorithms and Applications.
12: Bayesian Networks, Introduction and Practical Applications.
13: Relevance Feedback in Content-Based Image Retrieval: A Survey.
14: Learning Structural Representations of Text Documents in Large Document Collections.
15: Neural Networks in Bioinformatics.
Contributors.
Author Index.
Editors.

Handbook on Neural Information Processing.css-1p8rvzy{position:absolute;padding-left:8px;}

Handbook on Neural Information Processing