GPstruct: Bayesian structured prediction using Gaussian processes

Bratieres, Sebastien, Quadrianto, Novi and Ghahramani, Zoubin (2015) GPstruct: Bayesian structured prediction using Gaussian processes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (7). pp. 1514-1520. ISSN 0162-8828

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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 (M ^3 N), 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.

Item Type: Article
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: Q Science > QA Mathematics > QA0273 Probabilities. Mathematical statistics
Depositing User: Novi Quadrianto
Date Deposited: 18 Jun 2015 19:54
Last Modified: 09 Mar 2017 21:18
URI: http://sro.sussex.ac.uk/id/eprint/54605

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