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020 _a9783319896298
_9978-3-319-89629-8
024 7 _a10.1007/978-3-319-89629-8
_2doi
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072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
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072 7 _aUYQ
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082 0 4 _a006.3
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245 1 0 _aComputational Intelligence for Pattern Recognition
_h[electronic resource] /
_cedited by Witold Pedrycz, Shyi-Ming Chen.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aVIII, 428 p. 151 illus., 118 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v777
505 0 _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.
520 _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.
650 0 _aComputational intelligence.
_97716
650 0 _aArtificial intelligence.
_93407
650 0 _aPattern recognition systems.
_93953
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aAutomated Pattern Recognition.
_931568
700 1 _aPedrycz, Witold.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_951781
700 1 _aChen, Shyi-Ming.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_951782
710 2 _aSpringerLink (Online service)
_951783
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319896281
776 0 8 _iPrinted edition:
_z9783319896304
776 0 8 _iPrinted edition:
_z9783030078195
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v777
_951784
856 4 0 _uhttps://doi.org/10.1007/978-3-319-89629-8
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78834
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