Error Estimation for Pattern Recognition

This comprehensive guide on error estimation explores model-based approaches and innovative estimators, making it essential for pattern recognition research.

This book is a pioneering work that delves into error estimation through a model-based perspective. It starts with foundational concepts related to classifiers and error estimators, moving towards more complex topics like distributional and Bayesian theory. The text addresses critical issues surrounding the scientific validity of pattern classification, making it an essential resource for researchers and practitioners alike.

Error Estimation for Pattern Recognition emphasizes the importance of error estimation, a vast and often misunderstood area that spans various research fields utilizing pattern classification. The authors introduce model-based methodologies and explore innovative error estimators, including bolstered and Bayesian approaches. The motivation for this book stems from the challenges posed by applying pattern recognition techniques to high-throughput data with limited replicates, an issue increasingly prevalent across multiple domains.

The initial chapters cover fundamental aspects of classification error estimation, such as definitions and methods for estimating test-set and training-set errors. Subsequent chapters provide insights into the performance and representation of training-set error estimators for diverse pattern classifiers. Additional features include the latest findings on error estimation accuracy, comparative performance analyses of various error estimation techniques, and interactive exercises designed to enhance learning. This book stands out as the first comprehensive guide dedicated solely to error estimation in the realm of pattern recognition.