Bayesian Structured Prediction Using Gaussian Processes

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 suppor...

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Bibliographic Details
Published in:arXiv.org 2013-07
Main Authors: Bratieres, Sebastien, Novi Quadrianto, Ghahramani, Zoubin
Format: Article
Language:English
Subjects:
Bayesian analysis
Computer simulation
Gaussian process
Markov analysis
Markov chains
Mathematical models
Monte Carlo simulation
Natural language processing
Segmentation
Support vector machines
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Summary: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.
ISSN:2331-8422