LEADER 04519nam 22005895 450 001 9910299936103321 005 20200629220221.0 010 $a3-319-89629-6 024 7 $a10.1007/978-3-319-89629-8 035 $a(CKB)4100000003359645 035 $a(DE-He213)978-3-319-89629-8 035 $a(MiAaPQ)EBC6295309 035 $a(PPN)226696812 035 $a(EXLCZ)994100000003359645 100 $a20180430d2018 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputational Intelligence for Pattern Recognition /$fedited by Witold Pedrycz, Shyi-Ming Chen 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (VIII, 428 p. 151 illus., 118 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v777 300 $aIncludes index. 311 $a3-319-89628-8 327 $aRobust 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. 330 $aThe book presents a comprehensive and up-to-date review of fuzzy pattern recognition. It carefully discusses a range of methodological and algorithmic issues, as well as implementations and case studies, and identifies the best design practices, assesses business models and practices of pattern recognition in real-world applications in industry, health care, administration, and business. Since the inception of fuzzy sets, fuzzy pattern recognition with its methodology, algorithms, and applications, has offered new insights into the principles and practice of pattern classification. Computational intelligence (CI) establishes a comprehensive framework aimed at fostering the paradigm of pattern recognition. The collection of contributions included in this book offers a representative overview of the advances in the area, with timely, in-depth and comprehensive material on the conceptually appealing and practically sound methodology and practices of CI-based pattern recognition. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v777 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aPattern recognition 606 $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aPattern recognition. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aPattern Recognition. 676 $a006.3 702 $aPedrycz$b Witold$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aChen$b Shyi-Ming$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299936103321 996 $aComputational Intelligence for Pattern Recognition$92528759 997 $aUNINA