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Bayesian Structured Prediction Using Gaussian Processes


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Type

Article

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Authors

Bratieres, Sebastien 
Quadrianto, Novi 

Abstract

We introduce a conceptually novel structured prediction model, GPstruct, which is kernelized, non-parametric and Bayesian, by design. We motivate the model with respect to existing approaches, among others, conditional random fields (CRFs), maximum margin Markov networks (M3N), and structured support vector machines (SVMstruct), which embody only a subset of its properties. We present an inference procedure based on Markov Chain Monte Carlo. The framework can be instantiated for a wide range of structured objects such as linear chains, trees, grids, and other general graphs. As a proof of concept, the model is benchmarked on several natural language processing tasks and a video gesture segmentation task involving a linear chain structure. We show prediction accuracies for GPstruct which are comparable to or exceeding those of CRFs and SVMstruct.

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Keywords

stat.ML, stat.ML, cs.LG

Journal Title

CoRR

Conference Name

Journal ISSN

0162-8828

Volume Title

37

Publisher

IEEE
Sponsorship
Engineering and Physical Sciences Research Council (EP/I036575/1)