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Output Space Search for Structured Prediction

TitleOutput Space Search for Structured Prediction
Publication TypeConference Paper
Year of Publication2012
AuthorsDoppa, J R., A. Fern, and P. Tadepalli
Conference Name29th International Conference on Machine Learning (ICML 2012)
Date Published06/2012
Conference LocationEdinburgh, Scotland
Abstract

We consider a framework for structured prediction based on search in the space of complete structured outputs. Given a structured input, an output is produced by running a time-bounded search procedure, guided by a learned cost function, and then returning the least cost output uncovered during the search. This framework can be instantiated for a wide range of search spaces and search procedures, and easily incorporates arbitrary structured-prediction loss functions. In this paper, we make two main technical contributions within this framework. First, we describe a novel approach to automatically defining an effective search space over structured outputs, which is able to leverage the availability of powerful classification learning algorithms. In particular, we define the limited-discrepancy search space and relate the quality of that space to the quality of learned classifiers. Second, we give a generic cost function learning approach that can be instantiated for a wide range of search procedures. The key idea is to learn a cost function that attempts to mimic the behavior of conducting searches guided by the true loss function. Our experiments on six benchmark domains demonstrate that using our framework with only a small amount of search is sufficient for significantly improving on state-of-the-art structured-prediction performance.

URLhttp://arxiv.org/abs/1206.6460