Error Estimation for Pattern Recognition

This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to more specialized classifiers, it covers important topics and essential issues pertaining to the scientific validity of pattern classification.

• Includes the latest results on accuracy of error estimation
• Analyzes the performance of cross-validation and bootstrap error estimators using simulation and model-based approaches
• End-of-chapter exercises
• Highly interactive computer-based exercises

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).