We consider learning in situations where the function used to classify examples may switch back and forth between a small number of different concepts during the course of learnin...
Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms co...
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi...
Background: Remote homology detection is a hard computational problem. Most approaches have trained computational models by using either full protein sequences or multiple sequenc...
Juliana S. Bernardes, Alessandra Carbone, Gerson Z...
Abstract. This paper deals with the issue of learning in multi-agent systems (MAS). Particularly, we are interested in BDI (Belief, Desire, Intention) agents. Despite the relevance...
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...