Scalable Large-Margin Online Learning for Structured Classification
We investigate large-margin online learning algorithms fo r large-scale structured classification tasks, focusing on a structuredoutput extension of MIRA, the multi-class classification algorithm of Cr ammer and Singer [5]. The extension approximates the parameter updat es in MIRA usingk-best structural decoding. We evaluate the algorithm on sev eral sequential classification tasks, showing that it offers a co mpetitive, numerically stable, and computationally scalable alternati ve to conditional random fields and maximum margin Markov networks.