1 research outputs found
Semi-Supervised Learning with Declaratively Specified Entropy Constraints
We propose a technique for declaratively specifying strategies for
semi-supervised learning (SSL). The proposed method can be used to specify
ensembles of semi-supervised learning, as well as agreement constraints and
entropic regularization constraints between these learners, and can be used to
model both well-known heuristics such as co-training and novel domain-specific
heuristics. In addition to representing individual SSL heuristics, we show that
multiple heuristics can also be automatically combined using Bayesian
optimization methods. We show consistent improvements on a suite of
well-studied SSL benchmarks, including a new state-of-the-art result on a
difficult relation extraction task