Search-based Structured Prediction

Details Search-based Structured Prediction Search-based Structured Prediction

2009-07-04

arxiv.org/abs/0907.0786v1

Machine Learning Journal 2009

Computer Science

Learning

Scientific paper

We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good performance on the structured prediction problem.

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