Petros Xanthopoulos, Panos M. Pardalos, Theodore B. Trafalis

Robust Data Mining (eBook, PDF)

• Format: PDF

Produktbeschreibung

Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise.

This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems.

This brief will appeal to theoreticians and data miners working in this field.

Produktdetails

• Verlag: Springer New York
• Erscheinungstermin: 28. November 2012
• Englisch
• ISBN-13: 9781441998781
• Artikelnr.: 37661530

Autorenporträt

Dr. Panos M. Pardalos is Distinguished Professor of Industrial and Systems Engineering at the University of Florida. He is also affiliated faculty member of the Computer Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. He is also the director of the Center for Applied Optimization. Dr. Pardalos obtained a PhD degree from the University of Minnesota in Computer and Information Sciences. He has held visiting appointments at Princeton University, DIMACS Center, Institute of Mathematics and Applications, FIELDS Institute, AT&T Labs Research, Trier University, Linkoping Institute of Technology, and Universities in Greece. He has received numerous awards including, University of Florida Research Foundation Professor, UF Doctoral Dissertation Advisor/Mentoring Award, Foreign Member of the Royal Academy of Doctors (Spain), Foreign Member Lithuanian Academy of Sciences, Foreign Member of the Ukrainian Academy of Sciences, Foreign Member of the Petrovskaya Academy of Sciences and Arts (Russia), and Honorary Member of the Mongolian Academy of Sciences. Dr. Pardalos received the degrees of Honorary Doctor from Lobachevski University (Russia) and the V.M. Glushkov Institute of Cybernetics (Ukraine), he is a fellow of AAAS, a fellow of INFORMS, and in 2001 he was awarded the Greek National Award and Gold Medal for Operations Research. Dr. Theodore B. Trafalis is a Professor in the School of Industrial Engineering at the University of Oklahoma. He earned his BS in mathematics from the University of Athens, Greece, his MS in Applied Mathematics, MSIE, and PhD in Operations Research from Purdue University. He is a member of ORSA, SIAM, Hellenic Operational Society, International Society of Multiple criteria Decision Making, and the International Society of Neural Networks. His is listed in the 1993/1994 edition of Who s Who in the World. He was a visiting Assistant Professor at Purdue University (1989-1990), an invited Research Fellow at Delft University of Technology, Netherlands (1996), and a visiting Associate Professor at Blaise Pascal University, France and at the Technical University of Crete (1998). He was also an invited visiting Associate Professor at Akita Prefectural University, Japan (2001). Petros Xanthopoulos received the Dipl. Eng. degree in electronics and computer engineering from the Electronics and Computer Engineering Department, Technical University of Crete, Chania, Greece, in 2005 and a MSc from industrial and systems engineering, University of Florida. He is currently working toward the PhD degree in the Industrial and Systems Engineering Department, University of Florida, Gainesville. He has published his work in journals of IEEE, Elsevier and Wiley and he has edited a special issue for the Journal of Combinatorial Optimization entitled Data Mining in Biomedicine. He is currently a research assistant at the Center for Applied Optimization, University of Florida.

Inhaltsangabe

1. Introduction.- 2. Least Squares Problems.- 3. Principal Component Analysis.- 4. Linear Discriminant Analysis.- 5. Support Vector Machines.- 6. Conclusion.

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1. Introduction.- 2. Least Squares Problems.- 3. Principal Component Analysis.- 4. Linear Discriminant Analysis.- 5. Support Vector Machines.- 6. Conclusion.

Rezensionen

From the reviews:
"The goal of the book is to provide a guide for junior researchers interested in pursuing theoretical research in data mining and robust optimization and has been developed so that each chapter can be studied independent of the others." (Hans Benker, Zentralblatt MATH, Vol. 1260, 2013)