Supervised and Unsupervised Learning for Data Science

Michael W. Berry (Redaktør) ; Azlinah Mohamed (Redaktør) ; Bee Wah Yap (Redaktør)

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. Les mer
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Vår pris: 928,-

(Paperback) Fri frakt!
Leveringstid: Sendes innen 21 dager
På grunn av Brexit-tilpasninger og tiltak for å begrense covid-19 kan det dessverre oppstå forsinket levering.

Om boka

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.

Fakta

Innholdsfortegnelse

Chapter1: 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.

Om forfatteren

Professor Michael W. Berry is a Full Professor in the Departments of Electrical Engineering and Computer Science (EECS) and Mathematics at the University of Tennessee, Knoxville. He served as Interim Department Head of Computer Science from January 2004 to June 2007, and as Associate Head in the Department of Electrical Engineering and Computer Science from July 2007 to July 2012. He worked in the Communications Product Division of IBM in Raleigh, NC for about 1 year before accepting a research staff position in the Center for Supercomputing Research and Development at the University of Illinois at Urbana-Champaign. In 1990, he received a PhD in Computer Science from the University of Illinois at Urbana-Champaign. Prof. Berry is the co-author of "Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods" (SIAM, 1994) and "Understanding Search Engines: Mathematical Modeling and Text Retrieval, Second Edition" (Bestseller, SIAM, 2005) and editor of "Computational Information Retrieval" (SIAM, 2001), "Survey of Text Mining: Clustering, Classification, and Retrieval" (Springer-Verlag, 2003, 2007), "Lecture Notes in Data Mining" (Bestseller, World Scientific, 2006), "Text Mining: Applications and Theory" (Wiley, 2010), and "High-Performance Scientific Computing" (Springer, 2012). He has published well over 150 peer-refereed journal and conference publications and book chapters. He has organized numerous workshops on Text Mining and was Conference Co-Chair of the 2003 SIAM Third International Conference on Data Mining (May 1-3) in San Francisco, CA. He was Program Co-Chair of the 2004 SIAM Fourth International Conference on