Quasi-supervised learning for biomedical data analysis
Pattern Recognition
Selecting training points for one-class support vector machines
Pattern Recognition Letters
An affinity-based new local distance function and similarity measure for kNN algorithm
Pattern Recognition Letters
A segmental non-parametric-based phoneme recognition approach at the acoustical level
Computer Speech and Language
DCPE co-training for classification
Neurocomputing
A multiclass, k-NN approach to Bayes risk estimation
Pattern Recognition Letters
| Hi-index | 754.84 |
Nonparametric estimation of the Bayes riskR^astusing ak-nearest-neighbor (k-NN) approach is investigated. Estimates of the conditional Bayes errorr(X)for use in an unclassified test sample approach to estimateR^astare derived using maximum-likelihood estimation techniques. By using the volume information as well as the class representations of thek-NN's toX, the mean-squared error of the conditional Bayes error estimate is reduced significantly. Simulations are presented to indicate the performance of the estimates using unclassified testing samples.