Learning Multiple Tasks using Shared Hypotheses
Koby Crammer, Yishay Mansour · 2012
In this work we consider a setting where we have a very large number of related tasks with few examples from each individual task. Rather than either learning each task individually (and having a large generalization error) or learning all the tasks together using a single hypothesis (and suffering a potentially large inherent error), we consider learning a small pool of shared hypotheses. Each task is then mapped to a single hypothesis in the pool (hard association). We derive VC dimen-sion generalization bounds for our model, based on the number of tasks, shared hypothesis and the VC dimension of the hypotheses class. We conducted exper-iments with both synthetic problems and sentiment of reviews, which strongly support our approach. 1