This book explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to understand and implement. Sharing insights from international KDDM experts, it presents powerful strategies, models, and techniques relevant to the different stages of the KDDM process. The expert contributors discuss useful techniques and approaches for managing the full cycle of knowledge discovery projects to provide readers with a practical understanding of how to implement real-world KDDM projects beyond the single data mining step.
This book explains the knowledge discovery and data mining (KDDM) process in a manner that makes it easy for readers to understand and implement. Sharing insights from international KDDM experts, it presents powerful strategies, models, and techniques relevant to the different stages of the KDDM process. The expert contributors discuss useful techniques and approaches for managing the full cycle of knowledge discovery projects to provide readers with a practical understanding of how to implement real-world KDDM projects beyond the single data mining step.
Kweku-Muata Osei-Bryson is a professor of information systems (IS) at Virginia Commonwealth University in Richmond, Virginia, where he also served as the coordinator of the IS PhD program during 2001¿2003. He is also a visiting professor of computing at the University of the West Indies at Mona, Kingston, Jamaica. Previously, he was a professor of information systems and decision sciences at Howard University in Washington, D.C., United States. He has also worked as an IS practitioner in the industry and government. He holds a doctorate degree in applied mathematics (management science and information systems) from the University of Maryland at College Park; an MS degree in systems engineering from Howard University, and a bachelor¿s degree in natural sciences from the University of the West Indies at Mona, Kingston, Jamaica. His research areas include data mining, decision support systems, knowledge management, IS security, e-Commerce, information technology for development, database management, IS outsourcing, and multicriteria decision making. Corlane Barclay is a business consultant and a full-time lecturer at the University of Technology, Jamaica, since 2009, where she has designed and successfully implemented the first and only wholly owned graduate program in information systems management, with five specializations, of the School of Computing and Information Technology in 2011. She also served as a coordinator for this program between 2011 and 2012. She is a certified project manager, with a PMP® certification, with over 10 years of industry and government experience. She also holds a doctorate degree in information systems and an MS degree in information systems and bachelor¿s degree in management and accounting and law from the University of the West Indies, Mona campus. She is currently in the final year at the Norman Manley Law School, Mona, Kingston, Jamaica, completing the certificate of legal education, which prepares for admission to practice in the Commonwealth Caribbean territories. Her research interests include cyber security and cybercrime, project performance and project success, technology and telecommunications law, information and communication technologies for development, and knowledge discovery and data mining models.
Inhaltsangabe
Introduction to Reinforcement Learning. Model-Free Policy Iteration. Policy Iteration with Value Function Approximation. Basis Design for Value Function Approximation. Sample Reuse in Policy Iteration. Active Learning in Policy Iteration. Robust Policy Iteration. Model-Free Policy Search. Direct Policy Search by Gradient Ascent. Direct Policy Search by Expectation-Maximization. Policy-Prior Search. Model-Based Reinforcement Learning. Transition Model Estimation. Dimensionality Reduction for Transition Model Estimation.
Introduction to Reinforcement Learning. Model-Free Policy Iteration. Policy Iteration with Value Function Approximation. Basis Design for Value Function Approximation. Sample Reuse in Policy Iteration. Active Learning in Policy Iteration. Robust Policy Iteration. Model-Free Policy Search. Direct Policy Search by Gradient Ascent. Direct Policy Search by Expectation-Maximization. Policy-Prior Search. Model-Based Reinforcement Learning. Transition Model Estimation. Dimensionality Reduction for Transition Model Estimation.
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