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GPstruct: Bayesian structured prediction using Gaussian processes

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journal contribution
posted on 2023-06-08, 21:03 authored by Sebastien Bratieres, Novi QuadriantoNovi Quadrianto, Zoubin Ghahramani
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.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

IEEE Transactions on Pattern Analysis and Machine Intelligence

ISSN

0162-8828

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Issue

7

Volume

37

Page range

1514-1520

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2015-06-18

First Open Access (FOA) Date

2015-06-18

First Compliant Deposit (FCD) Date

2015-06-18

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