## Sequence Labelling SVMs Trained in One Pass

*Abstract*:
This paper proposes an online solver of the dual formulation
of support vector machines for structured output spaces. We apply it to
sequence labelling using the exact and greedy inference schemes. In both
cases, the per-sequence training time is the same as a perceptron based
on the same inference procedure, up to a small multiplicative constant.
Comparing the two inference schemes, the greedy version is much faster.
It is also amenable to higher order Markov assumptions and performs
similarly on test. In comparison to existing algorithms, both versions
match the accuracies of batch solvers that use exact inference after a
single pass over the training examples.

**Sequence Labelling SVMs Trained in One Pass**,

*Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008*, 146-161, Edited by Walter Daelemans, Bart Goethals and Katharina Morik, Lecture Notes in Computer Science, LNCS 5211, Springer, 2008.

@inproceedings{bordes-usunier-bottou-2008, author = {Bordes, Antoine and Usunier, Nicolas and Bottou, L\'{e}on}, title = {Sequence Labelling SVMs Trained in One Pass}, booktitle = {Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008}, year = {2008}, editor = {Daelemans, Walter and Goethals, Bart and Morik, Katharina}, series = {Lecture Notes in Computer Science, LNCS~5211}, pages = {146-161}, publisher = {Springer}, url = {http://leon.bottou.org/papers/bordes-usunier-bottou-2008}, }

### Implementation

Antoine Bordes provides an implementation of the LaRank algorithm which was in fact written for this paper. It also contains a special case for linear kernels.