Intelligent Data Analysis for Real-Life Applications: Theory and Practice
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Intelligent Data Analysis for Real-Life Applications: Theory and Practice

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Release Date: June, 2012|Copyright: © 2012 |Pages: 444
DOI: 10.4018/978-1-4666-1806-0
ISBN13: 9781466618060|ISBN10: 146661806X|EISBN13: 9781466618077
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Description & Coverage
Description:

With the recent and enormous increase in the amount of available data sets of all kinds, applying effective and efficient techniques for analyzing and extracting information from that data has become a crucial task.

Intelligent Data Analysis for Real-Life Applications: Theory and Practice investigates the application of Intelligent Data Analysis (IDA) to these data sets through the design and development of algorithms and techniques to extract knowledge from databases. This pivotal reference explores practical applications of IDA, and it is essential for academic and research libraries as well as students, researchers, and educators in data analysis, application development, and database management.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Computational Intelligence
  • Data Mining
  • Decision and regression trees
  • Graphical models
  • Information Retrieval
  • Intelligent data analysis applications
  • Knowledge-based systems
  • Scalable Algorithms
  • Swarm Intelligence
  • Systems application
Reviews & Statements

This handbook provides a state-of-the-art overview of Intelligent Data Analysis methods and a wide range of application domains. Each contribution in this book is clear evidence of the tremendous potential of Machine Learning techniques to make the computers of tomorrow 'smarter.'

– Adam E Gaweda, University of Louisville, USA

The 18 papers in this collection explore machine learning techniques for extracting information from large amounts of data with several variables. Six papers from Spanish universities review Bayesian network classifiers, apply computer vision to sorting fruit, describe data visualization for industrial processes, and propose agent-based systems for reading engineering sketches. Other topics include landmark sliding from 3D shape correspondence, detecting impact craters in planetary images, probabilistic graphical models for sports video mining, automatic text classification from labeled and unlabeled data, and the impact of enterprises' organizational quality on their economic results. B&w images and diagrams are provided.

– Book News Inc. Portland, OR
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Editor/Author Biographies

Rafael Magdalena-Benedito was born in 1968 in Segovia (Castellón). Between 1986 and 1991, he studied a degree in Physics at the University of Valencia, Computer and Electronic specialty. In 1993 he worked in the company of electro DextroMédica as dictafonía technician in cardiology and medical physics. In 1998 he joined the Group Processing Digital Signal Department, where he currently works on research projects, is an Assistant Professor. His areas of work are data security, multimedia networks, standardization in biomedical engineering, and telemedicine.

Marcelino Martínez-Sober received his B.S. and Ph.D. degrees in Physics in 1992 and 2000, respectively, from the Universitat de Valencia(Spain). Since 1994, he has been with the Digital Signal Processing Group at the Department of Electronics Engineering. He is an Assistant Professor. He has worked on several industrial projects with private companies (in the areas such as industrial control, real-time signal processing, and digital control) and with public funds (in the areas of fetal electrocardiography and ventricular fibrillation). His research interests include real time signal processing, digital control using DSP, and biomedical signal processing, with special interest in developing real time algorithms for non-invasive fetal electrocardiogram extraction.

José M. Martínez-Martínez received the B.Eng. degree in Telecommunication Engineering in 2006, and the M.Eng degree in Electronics Engineering in 2009, both from the University of Valencia, Spain. He is currently working towards the Ph.D. degree in the IDAL research group, University of Valencia. His research interest is machine learning methods, data mining, and data visualization.

Joan Vila Francés has studied Technical Engineering in Telecommunication and Electronics Engineering, both finished with first class honors, in the University of Valencia (Spain). In 2009 he received a PhD Degree in Electronics Engineering from the same university, where he currently holds a Lecturer position. His teaching is focused on Programmable Digital Systems. He has skills in industrial systems, digital electronics, computing, database management, and Web development.

Pablo Escandell-Montero received the B.Eng. degree in Telecommunications Engineering in 2006, and the M.Eng degree in Electronics Engineering in 2009, both from the University of Valencia, Spain. Currently, he is working towards the Ph.D. degree in the IDAL research group in the same university. His research interest is machine learning and data mining.

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Editorial Advisory Board
  • Darko Pevec, University of Ljubljana, Slovenia
  • Elena Sapozhnikova, University of Konstanz, Germany
  • Shigeaki Sakurai, Tokyo Institute of Technology, Japan
  • José Antonio Gámez, University of Castilla – La Mancha (UCLM), Spain
  • Ricardo Vilalta, University of Houston, USA