Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (2 Volumes)

Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (2 Volumes)

Emilio Soria Olivas, José David Martín Guerrero, Marcelino Martinez-Sober, Jose Rafael Magdalena-Benedito, Antonio José Serrano López
Indexed In: SCOPUS View 1 More Indices
Release Date: August, 2009|Copyright: © 2010 |Pages: 852
DOI: 10.4018/978-1-60566-766-9
ISBN13: 9781605667669|ISBN10: 1605667668|EISBN13: 9781605667676
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Description & Coverage
Description:

The machine learning approach provides a useful tool when the amount of data is very large and a model is not available to explain the generation and relation of the data set.

The Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques provides a set of practical applications for solving problems and applying various techniques in automatic data extraction and setting. A defining collection of field advancements, this Handbook of Research fills the gap between theory and practice, providing a strong reference for academicians, researchers, and practitioners.

Coverage:

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

  • Anomaly Detection
  • Classification with incomplete data
  • Cluster analysis and applications
  • Clustering and visualization
  • Machine Learning Applications
  • Machine Learning Models
  • Machine learning trends
  • Multi-Objective Optimization
  • Neural Networks
  • Principal graphs and manifolds
Reviews & Statements

This handbook covers exploratory as well as predictive modelling, frequentist and Bayesian methods which form a fruitful branch of machine learning in their own right. This extends beyond the design of non-linear algorithms to encompass also their evaluation, a critical and often neglected area of research, yet a critical stage in practical applications.

– Paulo J.G. Lisboa, Liverpool John Moores University, UK
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Editor/Author Biographies
Emilio Soria received an MS degree in physics (1992) and a PhD degree (1997) in electronics engineering from the Universitat de Valencia (Spain). He has been an assistant professor at the University of Valencia since 1997. His research is centered mainly in the analysis and applications of adaptive and neural systems.
José David Martín-Guerrero received a BS degree in physics (1997), a BS degree in electronics engineering (1999), an MS degree in electronic engineering (2001) and a PhD degree in electronic engineering (2004) from the University of Valencia (Spain). He is currently an assistant professor in the Department of Electronic Engineering, University of Valencia. His research interests include machine learning algorithms and their potential real application. Lately, his research has been especially focused on the study of reinforcement learning algorithms.
Marcelino Martinez received his BS and PhD degrees in physics (1992 and 2000, respectively) from the Universitat de Valencia (Spain). Since 1994 he has been with the Digital Signal Processing Group in the Department of Electronics Engineering. He is currently 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 foetal electrocardiography and ventricular fibrillation). His research interests include real time signal processing, digital control using DSP, and biomedical signal processing.
Rafael Magdalena received an MS and PhD degree in physics from the University of Valencia (Spain, 1991 and 2000 respectively). He has also been a lecturer with the Politechnic University of Valencia, a funded researcher with the research association in optics and has held industrial positions with several electromedicine and IT companies. Currently, he is a labour lecturer in electronic engineering with the University of Valencia (since 1998). He has conducted research in telemedicine, biomedical engineering, and signal processing. He is a Member of the IEICE.
Antonio J. Serrano received a BS degree in physics (1996), an MS degree in physics (1998) and a PhD degree in electronics engineering (2002) from the University of Valencia. He is currently an associate professor in the electronics engineering department at the same university. His research interest is machine learning methods for biomedical signal processing.
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Editorial Advisory Board

Editorial Advisory Board

  • Todor Ganchev, University of Patras, Greece
  • Pedro J. García-Laencina, Universidad Politécnica De Cartagena, Spain
  • Roy Rada, UMBC, USA
  • Vadlamani Ravi, Institute for Development and Research in Banking Technology (IDRBT), India
  • Marina Sokolova, CHEO Research Institute, Canada

    List of Reviewers

  • Tsan-Ming Choi, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
  • Stefano Ferilli, Università degli Studi di Bari, Italy
  • Todor Ganchev, University of Patras, Greece
  • Pedro J. García-Laencina, Universidad Politécnica De Cartagena. Spain
  • Adam E. Gaweda, University of Louisville, USA
  • Jose David Martín Guerrero, Universitat de València, Spain
  • Paul Honeine, Institut Charles Delaunay (FRE CNRS 2848), France
  • Chin Kim On, Universiti Malaysia Sabah, Malaysia
  • Roy Rada, UMBC, USA
  • V. Ravi, Institute for Development and Research in Banking Technology (IDRBT), India
  • Albert Ali Salah, Centre for Mathematics and Computer Science (CWI), The Netherlands
  • Jude Shavlik, University of Wisconsin, USA
  • Marina Sokolova, CHEO Research Institute, Canada
  • Emilio Soria, Valencia University, Spain
  • Rui Xu, Missouri University of Science and Technology, USA