Dealing with small data: On the generalization of context trees
published: Dec. 5, 2015, recorded: October 2015, views: 1379
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Context trees (CT) are a widely used tool in machine learning for representing context-specific independences in conditional probability distributions. Parsimonious context trees (PCTs) are a recently proposed generalization of CTs that can enable statistically more efficient learning due to a higher structural flexibility, which is particularly useful for small-data settings. However, this comes at the cost of a computationally expensive structure learning algorithm, which is feasible only for domains with small alphabets and tree depths. In this work, we investigate to which degree CTs can be generalized to increase statistical efficiency while still keeping the learning computationally feasible. Approaching this goal from two different angles, we (i) propose algorithmic improvements to the PCT learning algorithm, and (ii) study further generalizations of CTs, which are inspired by PCTs, but trade structural flexibility for computational efficiency. By empirical studies both on simulated and real-world data, we demonstrate that the synergy of combining of both orthogonal approaches yields a substantial improvement in obtaining statistically efficient and computationally feasible generalizations of CTs.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !