Emerging Methods in Predictive Analytics | Risk Management and Decision-Making

William H. Hsu

ISBN 10: 146665063X / ISBN 13: 9781466650633

Published by Information Science Reference, 2014

About this Item

Emerging Methods in Predictive Analytics | Risk Management and Decision-Making | William H. Hsu | Buch | Gebunden | Englisch | 2014 | Information Science Reference | EAN 9781466650633 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 105274650

Bibliographic Details

• Title: Emerging Methods in Predictive Analytics | Risk Management and Decision-Making
• Publisher: Information Science Reference
• Publication Date: 2014
• Language: English
• ISBN 10: 146665063X
• ISBN 13: 9781466650633
• Binding: Buch
• Condition: Neu
• Dimensions: 286W x 221H x 29D millimeters 286 millimeters width by 221 millimeters height by 29 millimeters depth
• Weight: 1379 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Decision making tools are essential for the successful outcome of any organization. Recent advances in predictive analytics have aided in identifying particular points of leverage where critical decisions can be made. Emerging Methods in Predictive Analytics: Risk Management and Decision Making provides an interdisciplinary approach to predictive analytics; bringing together the fields of business, statistics, and information technology for effective decision making. Managers, business professionals, and decision makers in diverse fields will find the applications and cases presented in this text essential in providing new avenues for risk assessment, management, and predicting the future outcomes of their decisions.

About the Author

William H. Hsu is an associate professor of Computing and Information Sciences at Kansas State University. He received a B.S. in Mathematical Sciences and Computer Science and an M.S.Eng. in Computer Science from Johns Hopkins University in 1993, and a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1998. His dissertation explored the optimization of inductive bias in supervised machine learning for predictive analytics. At the National Center for Supercomputing Applications (NCSA) he was a co-recipient of an Industrial Grand Challenge Award for visual analytics of text corpora. His research interests include machine learning, probabilistic reasoning, and information visualization, with applications to cybersecurity, education, digital humanities, geoinformatics, and biomedical informatics. Published applications of his research include structured information extraction; spatiotemporal event detection for veterinary epidemiology, crime mapping, and opinion mining; analysis of heterogeneous information networks. Current work in his lab deals with: data mining and visualization in education research; graphical models of probability and utility for information security; developing domain-adaptive models of large natural language corpora and social media for text mining, link mining, sentiment analysis, and recommender systems. Dr. Hsu has over 50 refereed publications in conferences, journals, and books, plus over 35 additional publications.