The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. 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.

Robust Constrained Concept Factorization.- An Automatic Cycling Performance Measurement System Based on ANFIS.- Fuzzy Classifiers Learned Through SVMs With Application to Specific Object Detection and Shape Extraction Using an RGB-D Camera.- Low Cost Parkinson’s Disease Early Detection and Classification Based on Voice and Electromyography Signal.- Particle Swarm Optimization Based HMM Parameter Estimation for Spectrum Sensing in Cognitive Radio System.- Improving Sparse Representation-Based Classification Using Local Principal Component Analysis.- Fuzzy Choquet Integration of Deep Convolutional Neural Networks for Remote Sensing.- Computational Intelligence for Pattern Recognition in EEG Signals.- Neural Network Based Physical Disorder Recognition for Elderly Health Care.- Deep Neural Networks for Structured Data.- Recognizing Subtle Micro-Facial Expressions Using Fuzzy Histogram of Optical Flow Orientations and Feature Selection Methods.- Granular Computing Techniques for Bioinformatics Pattern Recognition Problems in Non-Metric Spaces.- Multi-Classifier-Systems: Architectures, Algorithms and Applications.- Learning Label Dependency and Label Preference Relations in Graded Multi-Label Classification.- Improved Deep Neural Network Object Tracking System for Applications in Home Robotics.

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.

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

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