Computational Intelligence for Pattern Recognition

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 reco...

Ful tanımlama

Detaylı Bibliyografya
Müşterek Yazar: SpringerLink (Online service)
Diğer Yazarlar: Pedrycz, Witold (Editör), Chen, Shyi-Ming (Editör)
Materyal Türü: e-Kitap
Dil:İngilizce
Baskı/Yayın Bilgisi: Cham : Springer International Publishing : Imprint: Springer, 2018.
Edisyon:1st ed. 2018.
Seri Bilgileri:Studies in Computational Intelligence, 777
Konular:
Computational intelligence.
Artificial intelligence.
Pattern recognition systems.
Computational Intelligence.
Artificial Intelligence.
Automated Pattern Recognition.
Online Erişim:Full-text access
OPAC'ta görüntüle
İçindekiler:
  • 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.

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