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Computational Intelligence for Pattern Recognition

  • Book
  • © 2018

Overview

Editors:
  1. Witold Pedrycz

    1. University of Alberta, Edmonton, Canada

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    You can also search for this editor in PubMed   Google Scholar

  2. Shyi-Ming Chen

    1. National Taiwan University of Science and Technology, Taipei, Taiwan

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    You can also search for this editor in PubMed   Google Scholar

  • Provides a comprehensive and up-to-date overview, covering a spectrum of methodological and algorithmic issues, and discussing implementations and case studies
  • Identifies best design practices, assessing business models and pattern recognition in industry, health care, administration, and business
  • Offers a systematic introduction to the concepts, design methodology, and detailed algorithms
  • Includes individual chapters with clearly defined structures and well-defined focuses and additional reading material available via carefully selected references

Part of the book series: Studies in Computational Intelligence (SCI, volume 777)

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-viii
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  2. Fuzzy Choquet Integration of Deep Convolutional Neural Networks for Remote Sensing

    • Derek T. Anderson, Grant J. Scott, Muhammad Aminul Islam, Bryce Murray, Richard Marcum
    Pages 1-28

  3. Deep Neural Networks for Structured Data

    • Monica Bianchini, Giovanna Maria Dimitri, Marco Maggini, Franco Scarselli
    Pages 29-51

  4. Granular Computing Techniques for Bioinformatics Pattern Recognition Problems in Non-metric Spaces

    • Alessio Martino, Alessandro Giuliani, Antonello Rizzi
    Pages 53-81

  5. Multi-classifier-Systems: Architectures, Algorithms and Applications

    • Peter Bellmann, Patrick Thiam, Friedhelm Schwenker
    Pages 83-113

  6. Learning Label Dependency and Label Preference Relations in Graded Multi-label Classification

    • Khalil Laghmari, Christophe Marsala, Mohammed Ramdani
    Pages 115-164

  7. Improving Sparse Representation-Based Classification Using Local Principal Component Analysis

    • Chelsea Weaver, Naoki Saito
    Pages 165-206

  8. Robust Constrained Concept Factorization

    • Wei Yan, Bob Zhang
    Pages 207-225

  9. An Automatic Cycling Performance Measurement System Based on ANFIS

    • Andre Vieira Pigatto, Alexandre Balbinot
    Pages 227-252

  10. Fuzzy Classifiers Learned Through SVMs with Application to Specific Object Detection and Shape Extraction Using an RGB-D Camera

    • Chia-Feng Juang, Guo-Cyuan Chen
    Pages 253-274

  11. Particle Swarm Optimization Based HMM Parameter Estimation for Spectrum Sensing in Cognitive Radio System

    • Yogesh Vineetha, E. S. Gopi, Shaik Mahammad
    Pages 275-290

  12. Computational Intelligence for Pattern Recognition in EEG Signals

    • Aunnoy K Mutasim, Rayhan Sardar Tipu, M. Raihanul Bashar, Md. Kafiul Islam, M. Ashraful Amin
    Pages 291-320

  13. Neural Network Based Physical Disorder Recognition for Elderly Health Care

    • Sriparna Saha, Raktim Das
    Pages 321-339

  14. Recognizing Subtle Micro-facial Expressions Using Fuzzy Histogram of Optical Flow Orientations and Feature Selection Methods

    • S. L. Happy, Aurobinda Routray
    Pages 341-368

  15. Improved Deep Neural Network Object Tracking System for Applications in Home Robotics

    • Berat A. Erol, Abhijit Majumdar, Jonathan Lwowski, Patrick Benavidez, Paul Rad, Mo Jamshidi
    Pages 369-395

  16. Low Cost Parkinson’s Disease Early Detection and Classification Based on Voice and Electromyography Signal

    • Farika T. Putri, Mochammad Ariyanto, Wahyu Caesarendra, Rifky Ismail, Kharisma Agung Pambudi, Elta Diah Pasmanasari
    Pages 397-426

  17. Back Matter

    Pages 427-428
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Keywords

About this book

The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. It carefully discusses a range of methodological and algorithmic issues, as well as implementations and case studies, and identifies the best design practices, assesses business models and practices of pattern recognition in real-world applications in industry, health care, administration, and business. Since the inception of fuzzy sets, fuzzy pattern recognition with its methodology, algorithms, and applications, has offered new insights into the principles and practice of pattern classification. Computational intelligence (CI) establishes a comprehensive framework aimed at fostering the paradigm of pattern recognition. The collection of contributions included in this book offers a representative overview of the advances in the area, with timely, in-depth and comprehensive material on the conceptually appealing and practically sound methodology and practices of CI-based pattern recognition.

Editors and Affiliations

  • University of Alberta, Edmonton, Canada

    Witold Pedrycz

  • National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

Bibliographic Information

  • Book Title: Computational Intelligence for Pattern Recognition

  • Editors: Witold Pedrycz, Shyi-Ming Chen

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-319-89629-8

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing AG, part of Springer Nature 2018

  • Hardcover ISBN: 978-3-319-89628-1Published: 15 May 2018

  • Softcover ISBN: 978-3-030-07819-5Published: 30 January 2019

  • eBook ISBN: 978-3-319-89629-8Published: 30 April 2018

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: VIII, 428

  • Number of Illustrations: 33 b/w illustrations, 118 illustrations in colour

  • Topics: Computational Intelligence, Artificial Intelligence, Pattern Recognition

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