Dawn E Holmes

Data Mining: Foundations and Intelligent Paradigms: VOLUME 2: Statistical, Bayesian, Time Series and other Theoretical Aspects - Intelligent Systems Reference Library Dawn E Holmes 2012 edition

Marc Notes: Includes bibliographical references and author index. Table of Contents: From the content: Data Mining with Multilayer Perceptrons and Support Vector Machines.- Regulatory Networks under Ellipsoidal Uncertainty - Data Analysis and Prediction by Optimization Theory and Dynamical Systems.- A Visual Environment for Designing and Running Data Mining Workflows in the Knowledge Grid.- Formal framework for the Study of Algorithmic Properties of Objective Interestingness Measures.- Nonnegative Matrix Factorization: Models, Algorithms and Applications.- Visual Data Mining and Discovery with Binarized Vectors. Jacket Description/Back: Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 2 of this three volume series, we have brought together contributions from some of the most prestigious researchers in theoretical data mining. Each of the chapters is self contained. Statisticians and applied scientists/ engineers will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in data mining. Publisher Marketing: There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled Data Mining: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Contributor Bio:  Jain, Lakhmi C Jain is director/founder of the Knowledge-Based Intelligent Engineering Systems Centre, located in the Division of Information Technology, Engineering and the Envvironment. He is a fellow of the Institution of Engineers, Australia.