In this paper, we study cost-sensitive semi-supervised learning where many of the training examples are unlabeled and different misclassification errors are associated with unequa...
We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
Designing a computer-supported learning scenario involving a constructivist approach of learning lays on a paradox. On the one hand, learning flows must be precisely described –...
Anne Lejeune, Muriel Ney, Armin Weinberger, Margus...
Support Vector Machine (SVM) has been spotlighted in the machine learning community thanks to its theoretical soundness and practical performance. When applied to a large data set...
In order to claim fully general intelligence in an autonomous agent, the ability to learn is one of the most central capabilities. Classical machine learning techniques have had ma...