Recent advances in error rate estimation
Pattern Recognition Letters
Bootstrap Techniques for Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Effects of Sample Size in Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Pattern Recognition and Valiant's Learning Framework
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Generalization Ability of Neural Network Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
An unsupervised and non-parametric Bayesian classifier
Pattern Recognition Letters
A General Model for Finite-Sample Effects in Training and Testing of Competing Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamenta Informaticae
Improving nearest neighbor classification with cam weighted distance
Pattern Recognition
Parasite detection and identification for automated thin blood film malaria diagnosis
Computer Vision and Image Understanding
Expert Systems with Applications: An International Journal
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Nearest neighbor classification using cam weighted distance
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Model selection and assessment for classification using validation
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Classifier variability: Accounting for training and testing
Pattern Recognition
Fundamenta Informaticae
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An expression for expected classifier performance previously derived by the authors is applied to a variety of error estimation methods and a unified and comprehensive approach to the analysis of classifier performance is presented. After the error expression is introduced, it is applied to three cases: (1) a given classifier and a finite test set; (2) given test distributions a finite design set; and (3) finite and independent design and test sets. For all cases, the expected values and variances of the classifier errors are presented. Although the study of Case 1 does not produce any new results, it is important to confirm that the proposed approach produces the known results, and also to show how these results are modified when the design set becomes finite, as in Cases 2 and 3. The error expression is used to compute the bias between the leave-one-out and resubstitution errors for quadratic classifiers. The effect of outliers in design samples on the classification error is discussed. Finally, the theoretical analysis of the bootstrap method is presented for quadratic classifiers.