000			04002nam a22003615i 4500
001			102373
003			DE-He213
005			20240111050139.0
007			cr nn 008mamaa
008			180430s2018 gw | s |||| 0|eng d
020			_a9783319896298
024	7		_a10.1007/978-3-319-89629-8 _2doi
040			_aES-MaUEC _bspa
050		4	_aQ327 _b2018 EB
245	1	0	_aComputational Intelligence for Pattern Recognition _cedited by Witold Pedrycz, Shyi-Ming Chen.
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (VIII, 428 páginas 151 ilustraciones, 118 ilustraciones a color)
347			_atext file _bPDF
490	0		_aStudies in Computational Intelligence _x1860-949X _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	3		_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		7	_9666321 _aIngeniería asistida por ordenador
650		7	_aInteligencia artificial _2embne _9413115
650		7	_aReconocimiento de formas _2embne _9152614
700	1		_aPedrycz, Witold _d1953- _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _1http://viaf.org/viaf/49305507/ _967495
700	1		_aChen, Shyi-Ming _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _0http://id.loc.gov/authorities/names/no2009163507 _1http://viaf.org/viaf/101512796/ _998849
776	0	8	_iEdición impresa: _z9783319896281
776	0	8	_iEdición impresa: _z9783319896304
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-89629-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
490	0		_aEngineering (Springer-11647)
988			_aEBSPRINGER_2018
998			_b12/2018 _dz _ea _feng _ggw _h0
999			_c102373 _d102373 _x1