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 ...
Probabilistic Load Forecasting Based on Adaptive Online Learning
Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent ...