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

Keywords: pattern recognition, machine learning, classification, error estimation, Bayesian methods, Pattern Analysis