LEADER 05070nam 22007095 450 001 9910366591103321 005 20200702221017.0 010 $a3-030-22475-9 024 7 $a10.1007/978-3-030-22475-2 035 $a(CKB)4100000009184958 035 $a(DE-He213)978-3-030-22475-2 035 $a(MiAaPQ)EBC5892498 035 $a(PPN)258064129 035 $a(EXLCZ)994100000009184958 100 $a20190904d2020 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSupervised and Unsupervised Learning for Data Science /$fedited by Michael W. Berry, Azlinah Mohamed, Bee Wah Yap 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (VIII, 187 p. 55 illus., 45 illus. in color.) 225 1 $aUnsupervised and Semi-Supervised Learning,$x2522-848X 311 $a3-030-22474-0 327 $aChapter1: A Systematic Review on Supervised & Unsupervised Machine Learning Algorithms for Data Science -- Chapter2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints -- Chapter3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout -- Chapter4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling -- Chapter5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application -- Chapter6: Semantic Unsupervised Learning for Word Sense Disambiguation -- Chapter7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network -- Chapter8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering. 330 $aThis book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, classification, clustering, topic modeling, graph analysis and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018). Includes new advances in clustering and classification using semi-supervised and unsupervised learning; Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning; Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning. 410 0$aUnsupervised and Semi-Supervised Learning,$x2522-848X 606 $aElectrical engineering 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aPattern recognition 606 $aArtificial intelligence 606 $aData mining 606 $aCommunications Engineering, Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/T24035 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aElectrical engineering. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aPattern recognition. 615 0$aArtificial intelligence. 615 0$aData mining. 615 14$aCommunications Engineering, Networks. 615 24$aSignal, Image and Speech Processing. 615 24$aPattern Recognition. 615 24$aArtificial Intelligence. 615 24$aData Mining and Knowledge Discovery. 676 $a621.382 676 $a006.31 702 $aBerry$b Michael W$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMohamed$b Azlinah$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYap$b Bee Wah$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910366591103321 996 $aSupervised and Unsupervised Learning for Data Science$92516837 997 $aUNINA