Now showing items 1-3 of 3
Minimax Classification with 0-1 Loss and Performance Guarantees
Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate ...
General supervision via probabilistic transformations
Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training samples. This ...
Nature-inspired approaches for distance metric learning in multivariate time series classification
The applicability of time series data mining in many different fields has motivated the scientific community to focus on the development of new methods towards improving the performance of the classifiers over this particular ...