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Adaptive Stochastic Dual Coordinate Ascent for Conditional Random Fields
[article]
2018
arXiv
pre-print
This work investigates the training of conditional random fields (CRFs) via the stochastic dual coordinate ascent (SDCA) algorithm of Shalev-Shwartz and Zhang (2016). SDCA enjoys a linear convergence rate and a strong empirical performance for binary classification problems. However, it has never been used to train CRFs. Yet it benefits from an 'exact' line search with a single marginalization oracle call, unlike previous approaches. In this paper, we adapt SDCA to train CRFs, and we enhance it
arXiv:1712.08577v2
fatcat:ocxs6k2jlzewzbvatauo4lwcey