Error Estimation For Pattern Recognition

This book is the first of its kind to focus on error estimation, which is a widespan and poorly understood topic that spans all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstering and Bayesian estimators. The idea for this book was motivated by the problem of pattern recognition using high-throughput biological data with limited replicates; however, the basic problems are now appearing in many areas.The first two chapters cover basic issues in classification error estimation, such as definitions, test-set error estimation and training-set error estimation. The remaining chapters in this book covers various pattern classifiers, as well as theories and representations of error-estimations amongst those classifiers.

Ulisses M. Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, USA. He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University. He received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing. He is an IEEE Senior Member.

Edward R. Dougherty is Distinguished Professor, Robert F. Kennedy ‘26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, USA. He is a fellow of both IEEE and SPIE, and he has received the SPIE Presidents Award. He has authored several books including Epistemology of the Cell: A Systems Perspective on Biological Knowledge and Random Processes for Image and Signal Processing (Wiley-IEEE Press).