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. ...
We consider the problem of characterisation of sequences of heterogeneous symbolic data that arise from a common underlying temporal pattern. The data, which are subject to impreci...
Several marketing problems involve prediction of customer purchase behavior and forecasting future preferences. We consider predictive modeling of large scale, bi-modal or multimo...
Commercial datasets are often large, relational, and dynamic. They contain many records of people, places, things, events and their interactions over time. Such datasets are rarel...
Andrew Fast, Lisa Friedland, Marc Maier, Brian Tay...
We consider the problem of constructing a directed acyclic graph that encodes temporal relations found in a text. The unit of our analysis is a temporal segment, a fragment of tex...
Philip Bramsen, Pawan Deshpande, Yoong Keok Lee, R...