In recent years, there has been a growing interest in using rich representations such as relational languages for reinforcement learning. However, while expressive languages have ...
Tom Croonenborghs, Jan Ramon, Hendrik Blockeel, Ma...
k. The model we study can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting. We show that the multi...
Online boosting is one of the most successful online learning algorithms in computer vision. While many challenging online learning problems are inherently multi-class, online boo...
Amir Saffari, Martin Godec, Thomas Pock, Christian...
We study online learning in an oblivious changing environment. The standard measure of regret bounds the difference between the cost of the online learner and the best decision in...
Active learning is a generic approach to accelerate training of classifiers in order to achieve a higher accuracy with a small number of training examples. In the past, simple ac...