Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques

Olivas, Emilio Soria; Guerrero, Jose David Martin; Sober, Marcelino Martinez; Benedito, Jose Rafael Magdalena; Lopez, Antonio Jose Serrano

ISBN 10: 1605667668 / ISBN 13: 9781605667669

Edité par Information Science Reference, 2009

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Détails bibliographiques

• Titre: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods and Techniques
• Éditeur: Information Science Reference
• Date de parution: 2009
• Langue: anglais
• ISBN à 10 chiffres: 1605667668
• ISBN à 13 chiffres: 9781605667669
• Reliure: Couverture rigide
• État de l'article: Neuf

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Synopsis

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.

À propos de l?auteur

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. Jose David Martin-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.