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...
We present a unified framework for designing polynomial time approximation schemes (PTASs) for “dense” instances of many NP-hard optimization problems, including maximum cut,...
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
The field of evolutionary dynamic optimisation is concerned with the application of evolutionary algorithms to dynamic optimisation problems. In recent years, numerous new algorith...