We consider an online learning setting where at each time step the decision maker has to choose how to distribute the future loss between k alternatives, and then observes the los...
Rescaling is possibly the most popular approach to cost-sensitive learning. This approach works by rescaling the classes according to their costs, and it can be realized in differ...
A framework for task assignment in heterogeneous computing systems is presented in this work. The framework is based on a learning automata model. The proposed model can be used f...
In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. ...
We study an extension of the "standard" learning models to settings where observing the value of an attribute has an associated cost (which might be different for differ...