Error Estimation For Pattern Recognition
Error Estimation For Pattern Recognition Photo

Error Estimation For Pattern Recognition

RUR 10419.59

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This book is the first of its kind to discuss error estimation with a model-based approach


Additional features of the book include The latest results on the accuracy of error estimation Performance analysis of re-substitution, cross-validation, and bootstrap error estimators using analytical and simulation approaches Highly interactive computer-based exercises and end-of-chapter problems This is the first book exclusively about error estimation for pattern recognition

Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A M University, USA

Braga Neto received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing

Dougherty 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 .

Dougherty is a Distinguished Professor, Robert F



Edward R

Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification

From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification

He is a fellow of both the IEEE and SPIE, and he has received the SPIE Presidents Award

He is an IEEE Senior Member

He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University

It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian estimators

Kennedy 26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A M University, USA

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 cover results on the performance and representation of training-set error estimators for various pattern classifiers

This book was motivated by the application of pattern recognition to high-throughput data with limited replicates, which is a basic problem now appearing in many areas

Ulisses M