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GPstruct: Bayesian structured prediction using Gaussian processes
journal contribution
posted on 2023-06-08, 21:03 authored by Sebastien Bratieres, Novi QuadriantoNovi Quadrianto, Zoubin GhahramaniWe 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.
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Publication status
- Published
File Version
- Accepted version
Journal
IEEE Transactions on Pattern Analysis and Machine IntelligenceISSN
0162-8828Publisher
Institute of Electrical and Electronics Engineers (IEEE)External DOI
Issue
7Volume
37Page range
1514-1520Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2015-06-18First Open Access (FOA) Date
2015-06-18First Compliant Deposit (FCD) Date
2015-06-18Usage metrics
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