On Supervised Classification and List Estimation

The main focus of this dissertation is to propose asymptotically optimal classifiers for the classification of feature vectors with independent and non-identically distributed elements. The proposed classifiers outperform the benchmark schemes when the number of training data is small, and the length of the feature vectors is sufficiently high.