LEADER 03986nam 22006015 450 001 9910254312803321 005 20200706040014.0 010 $a3-319-57115-X 024 7 $a10.1007/978-3-319-57115-7 035 $a(CKB)4340000000062384 035 $a(DE-He213)978-3-319-57115-7 035 $a(MiAaPQ)EBC5579059 035 $a(PPN)201471760 035 $a(EXLCZ)994340000000062384 100 $a20170502d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGranular Neural Networks, Pattern Recognition and Bioinformatics /$fby Sankar K. Pal, Shubhra S. Ray, Avatharam Ganivada 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIX, 227 p. 54 illus., 31 illus. in color.) 225 1 $aStudies in Computational Intelligence,$x1860-949X ;$v712 311 $a3-319-57113-3 320 $aIncludes bibliographical references and index. 327 $aIntroduction to Granular Computing, Pattern Recognition and Data Mining -- Classi?cation using Fuzzy Rough Granular Neural Networks -- Clustering using Fuzzy Rough Granular Self-Organizing Map -- Fuzzy Rough Granular Neural Network and Unsupervised Feature Selection. 330 $aThis book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinformatics applications. The book is recommended for both students and practitioners working in computer science, electrical engineering, data science, system design, pattern recognition, image analysis, neural computing, social network analysis, big data analytics, computational biology and soft computing. 410 0$aStudies in Computational Intelligence,$x1860-949X ;$v712 606 $aComputational intelligence 606 $aArtificial intelligence 606 $aBioinformatics 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 $aComputational Biology/Bioinformatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23050 615 0$aComputational intelligence. 615 0$aArtificial intelligence. 615 0$aBioinformatics. 615 14$aComputational Intelligence. 615 24$aArtificial Intelligence. 615 24$aComputational Biology/Bioinformatics. 676 $a006.32 700 $aPal$b Sankar K$4aut$4http://id.loc.gov/vocabulary/relators/aut$0117447 702 $aRay$b Shubhra S$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aGanivada$b Avatharam$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254312803321 996 $aGranular Neural Networks, Pattern Recognition and Bioinformatics$92169612 997 $aUNINA