Supervised and Unsupervised Learning for Data Science / Unsupervised and Semi-Supervised Learning (PDF)
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This 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.
Professor Dr Azlinah Mohamed is a Professor at the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Malaysia. She currently serves as the Dean of the faculty; she was previously the Special Officer to the Vice Chancellor and Head of the Academic Affairs and Development Unit of Universiti Teknologi MARA. She received her MSc (Artificial Intelligence) from University of Bristol, UK and PhD (Decision Support Systems) from Universiti Kebangsaan Malaysia. Her recent research activities and numerous professional publications in international conferences and local journals focus on her interests in the Artificial Intelligence, Decision Support Systems and Soft Computing. She has published well over 180 peer-refereed journal and conference publications and book chapters. She was the Honorary Chair of the 2015, 2016 and 2017 International Conference on Soft Computing in Data Science, and she was a keynote speaker at the 2016 International Conference on Soft Computing in Data Science (SCDS2016). She was also awarded with many competitive grants from ScienceFund, MOSTI and others on both academic and industrial projects for the industry, as well as for the government. Her research works includes the Information Professionals' Competency Assessment Model and the Multi-Parametric Pectin Lyase-Like Protein Function Classifier which had won many awards. She is also an active member of the Malaysia Information Technology Society (MITS), Lembaga Akredetasi Negara, Malaysia and Artificial Intelligence Society.
Professor Bee Wah Yap is a Professor at the Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Malaysia. She is the Head of Advanced Analytics Engineering Centre (AAEC), a Centre of Excellence in FSKM. She received her Bachelor of Science (Education)(Hons) degree, majoring in Mathematics from University of Science Malaysia, Master of Statistics from University of California Riverside and PhD (Statistics) from University of Malaya. Her research interests are in data mining, computational statistics and multivariate data analysis. She actively organizes SCDS2015, SCDS2016 and SCDS2017 conference which focus on Soft Computing in Data Science. She also actively conduct statistical workshops (IBM SPSS STATISTICS, IBM SPSS AMOS, PLS-SEM, SAS EMINER). She has published papers in ISI journals such as Expert Systems with Applications, Journal of Statistical Computation and Simulation, Communication in Statistics-Simulation and Computation, and also in Scopus indexed journals. She is also an active reviewer for international journals such as International Journal of Bank Marketing and Communication in Statistics-Simulation and Computation and Neurocomputing.
- 2019, 1st ed. 2020, 187 Seiten, Englisch
- Herausgegeben: Michael W. Berry, Azlinah Mohamed, Bee Wah Yap
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3030224759
- ISBN-13: 9783030224752
- Erscheinungsdatum: 04.09.2019
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- Grösse: 4.10 MB
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