Synopses & Reviews
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
Synopsis
This work explores current applications of robust optimization in data mining, offering an overview of this rapidly growing field and presenting machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems.
About the Author
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.
Table of Contents
-1. Introduction (Data Mining,Robust Optimization).-2. Robust Data Processing.-3. Robust Unsupervised Learning.-4. Robust Supervised Learning.-5. Conclusions and Future Trends.-References.