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 utilizing multi-core and multi-machine parallelism.
This project is released under the Apache License, Version 2.0.
Released the parallel Gibbs sampling code for Gibbs MedLDA topic model on March 10, 2013;
Released the non-parallel Gibbs sampling code for Gibbs MedLDA topic model on March 10, 2013;
Jun Zhu, Ning Chen, Hugh Perkins, and Bo Zhang.
Gibbs Max-margin Topic Models with Fast Sampling Algorithms,
In ICML, Atlanta, USA, 2013.