Learning Multiple Tasks using Shared Hypotheses

NeurIPS 2012 Koby CrammerYishay Mansour

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 {\em shared hypotheses}... (read more)

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