Existing max-margin supervised topic mod- els rely on an iterative procedure to solve multiple latent SVM subproblems with addi-.
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms. Jun Zhu dcszj ... The authors present Gibbs max-margin topic models by: ○ Instead of ...
Jun 16, 2013 · This paper presents Gibbs max-margin topic models by minimizing an expected margin loss, an upper bound of the existing margin loss derived from ...
This paper presents Gibbs max-margin supervised topic models, a latent variable Gibbs classifier to discover hidden topic representations for various tasks, ...
This paper presents Gibbs max-margin topic models by minimizing an expected margin loss, an upper bound of the existing margin loss derived from an expected ...
Abstract. Max-margin learning is a powerful approach to building classifiers and structured output predictors. Recent work on max-margin supervised topic ...
Oct 10, 2013 · Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an ...
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Gibbs max-margin supervised topic models minimize an expected margin loss, which is an upper bound of the existing margin loss derived from an expected ...
Gibbs MedLDA is a Max-margin Topic Model framework developed under Yahoo_LDA framework. It supports large-scale multi-class and multi-label classification by ...
Mar 1, 2017 · Bayesian max-margin models have shown superiority in various practical applications, such as text categorization, collaborative prediction, ...