The nature of statistical learning theory
The nature of statistical learning theory
Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
The Journal of Machine Learning Research
Almost-everywhere algorithmic stability and generalization error
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Permutation tests for classification
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Selection of relevant genes in cancer diagnosis based on their prediction accuracy
Artificial Intelligence in Medicine
An efficient algorithm for learning to rank from preference graphs
Machine Learning
Data mining of vector–item patterns using neighborhood histograms
Knowledge and Information Systems
Point-distribution algorithm for mining vector-item patterns
Proceedings of the ACM SIGKDD Workshop on Useful Patterns
Permutation Tests for Studying Classifier Performance
The Journal of Machine Learning Research
Permutation tests for classification
COLT'05 Proceedings of the 18th annual conference on Learning Theory
| Hi-index | 0.00 |
We describe a permutation procedure used extensively in classification problems in computational biology and medical imaging. We empirically study the procedure on simulated data and real examples from neuroimaging studies and DNA microarray analysis. A theoretical analysis is also suggested to assess the asymptotic behavior of the test. An interesting observation is that concentration of the permutation procedure is controlled by a Rademacher average which also controls the concentration of empirical errors to expected errors.