Effects of Sample Size in Classifier Design
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimation of Classifier Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
What Size Test Set Gives Good Error Rate Estimates?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Rule Induction: Combining Rough Set and Statistical Approaches
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Hybridization of rough sets and statistical learning theory
Transactions on rough sets XIII
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
| Hi-index | 0.00 |
In this paper we derive the bounds for Validation (known also as Hold-Out Estimate and Train-and-Test Method). We present the best possible bound in the case of 0-1 valued loss function. We also provide the tables where the least sample size is calculated that is necessary for obtaining the bound for a given estimation rate and reliability of estimation. For an arbitrary bounded loss function we present the optimal bound approximation with any given accuracy.