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019 ▼a 1138519880 ▼a 1139150933 ▼a 1146002651 ▼a 1149928099
020 ▼a 9781000025408 ▼q (electronic book)
020 ▼a 1000025403 ▼q (electronic book)
020 ▼a 9781000025361 ▼q (electronic book)
020 ▼a 1000025365 ▼q (electronic book)
020 ▼a 9780429270352 ▼q (electronic book)
020 ▼a 0429270356 ▼q (electronic book)
020 ▼z 0367211580
020 ▼z 9780367211585
0247 ▼a 10.1201/9780429270352 ▼2 doi
035 ▼a 2274423 ▼b (N$T)
035 ▼a (OCoLC)1137835525 ▼z (OCoLC)1138519880 ▼z (OCoLC)1139150933 ▼z (OCoLC)1146002651 ▼z (OCoLC)1149928099
037 ▼a 9780429270352 ▼b Taylor & Francis
040 ▼a YDX ▼b eng ▼e rda ▼e pn ▼c YDX ▼d TYFRS ▼d EBLCP ▼d UKMGB ▼d OCLCF ▼d N$T ▼d MFS ▼d UKAHL ▼d COO ▼d YDXIT ▼d OCLCQ ▼d 248032
049 ▼a MAIN
050 4 ▼a Q325.5 ▼b .N45 2020
072 7 ▼a COM ▼x 016000 ▼2 bisacsh
072 7 ▼a COM ▼x 018000 ▼2 bisacsh
072 7 ▼a COM ▼x 021030 ▼2 bisacsh
072 7 ▼a AKP ▼2 bicssc
08204 ▼a 006.312 ▼2 23
1001 ▼a Nelakurthi, Arun Reddy, ▼e author.
24510 ▼a Social media analytics for user behavior modeling : ▼b a task heterogeneity perspective / ▼c Arun Reddy Nelakurthi, Jingrui He. ▼h [electronic resource]
264 1 ▼a Boca Raton : ▼b CRC Press, ▼c [2020]
300 ▼a 1 online resource (xv, 97 pages) : ▼b illustrations
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a computer ▼b c ▼2 rdamedia
338 ▼a online resource ▼b cr ▼2 rdacarrier
4901 ▼a Data-enabled engineering
504 ▼a Includes bibliographical references and index.
520 ▼a In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.
5450 ▼a Arun Reddy Nelakurthi is a Senior Engineer in Machine Learning Research at Samsung Research America, Mountain View, California. He received his PhD in Machine Learning from Arizona State University in 2019. His research focuses on heterogeneous machine learning, transfer learning, user modeling and semi-supervised learning, with applications in social network analysis, social media analysis and healthcare informatics. He has served on the program committee for Conference on Information and Knowledge Management (CIKM) and The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). He also worked as a reviewer for IEEE Transactions on Knowledge and Data Engineering (TKDE), Data Mining and Knowledge Discovery (DMKD) and IEEE Transactions on Neural Networks and Learning Systems (TNNLS) journals. Jingrui He is an associate professor in the School of Information Sciences at the University of Illinois at Urbana-Champaign. She received her PhD in machine learning from Carnegie Mellon University in 2010. Her research focuses on heterogeneous machine learning, rare category analysis, active learning and semi-supervised learning, with applications in social network analysis, healthcare, and manufacturing processes. Dr. He is the recipient of the 2016 NSF CAREER Award and a threetime recipient of the IBM Faculty Award, in 2018, 2015 and 2014 respectively. She was selected for an IJCAI 2017 Early Career Spotlight, and was invited to the 24th CNSF Capitol Hill Science Exhibition. Dr. He has published more than 90 refereed articles, and is the author of the book, Analysis of Rare Categories (Springer- Verlag, 2011). Her papers have been selected as "Best of the Conference" by ICDM 2016, ICDM 2010, and SDM 2010. She has served on the senior program committee/ program committee for Knowledge Discovery and Data Mining (KDD), International Joint Conference on Artificial Intelligence (IJCAI), Association for the Advancement of Artificial Intelligence (AAAI), SIAM International Conference on Data Mining (SDM), and International Conference on Machine Learning (ICML).
5880 ▼a Online resource; title from digital title page (viewed on April 14, 2020).
590 ▼a Master record variable field(s) change: 050
650 0 ▼a Machine learning.
650 0 ▼a Data mining.
650 0 ▼a Social media.
650 0 ▼a Social networks.
650 7 ▼a COMPUTERS ▼x Computer Vision & Pattern Recognition. ▼2 bisacsh
650 7 ▼a COMPUTERS ▼x Data Processing ▼x General. ▼2 bisacsh
650 7 ▼a COMPUTERS ▼x Database Management ▼x Data Mining. ▼2 bisacsh
650 7 ▼a Data mining. ▼2 fast ▼0 (OCoLC)fst00887946
650 7 ▼a Machine learning. ▼2 fast ▼0 (OCoLC)fst01004795
650 7 ▼a Social media. ▼2 fast ▼0 (OCoLC)fst01741098
650 7 ▼a Social networks. ▼2 fast ▼0 (OCoLC)fst01122678
655 4 ▼a Electronic books.
7001 ▼a He, Jingrui, ▼e author.
77608 ▼i Print version: ▼z 9781000025408
77608 ▼i Print version: ▼z 0367211580 ▼z 9780367211585 ▼w (OCoLC)1121287160
830 0 ▼a Data-enabled engineering.
85640 ▼3 EBSCOhost ▼u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2274423
938 ▼a EBSCOhost ▼b EBSC ▼n 2274423
990 ▼a 관리자
994 ▼a 92 ▼b N$T