Computational Intelligence for Pattern Recognition

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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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.

• Computational Intelligence
• Pattern Recognition
• Fuzzy Pattern Recognition
• Pattern Classifiers
• Deep Learning

• University of Alberta, Edmonton, Canada

  Witold Pedrycz

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

  Shyi-Ming Chen

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