===== Guarantees for Approximate Incremental SVMs =====

//Abstract//:
Assume a teacher provides examples (x<sub>t</sub>,y<sub>t</sub>) one by one.
An approximate incremental SVM computes a sequence of 
classifiers that are close to the true SVM solutions 
computed on the successive incremental training sets. 
We show that simple algorithms can satisfy 
an averaged accuracy criterion with a computational 
cost that scales as well as the best SVM algorithms
with the number of examples. 
Finally, we exhibit some experiments
highlighting the benefits of joining fast incremental optimization 
and curriculum and active learning [Schohn and Cohn, 2000, 
Bordes et al., 2005, Bengio et al., 2009].


<box 99% orange>
Nicolas Usunier, Antoine Bordes and Léon Bottou:  **Guarantees for Approximate Incremental SVMs**,  //Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics//, 9:884-891, Edited by Yee Whye Teh and Mike Titterington, Chia Laguna Resort, Sardinia, Italy, May 2010.

[[http://leon.bottou.org/publications/djvu/aistat-2010.djvu|aistat-2010.djvu]]
[[http://leon.bottou.org/publications/pdf/aistat-2010.pdf|aistat-2010.pdf]]
[[http://leon.bottou.org/publications/psgz/aistat-2010.ps.gz|aistat-2010.ps.gz]]
</box>

  @inproceedings{usunier-bordes-bottou-2010,
    author = {Usunier, Nicolas and Bordes, Antoine and Bottou, L\'{e}on},
    title = {Guarantees for Approximate Incremental SVMs},
    booktitle = {Proceedings of the Thirteenth International 
                 Conference on Artificial Intelligence and Statistics},
    pages = {884-891},
    year = {2010},
    editor = {Teh, Yee Whye and Titterington, Mike},
    volume = {9},
    address = {Chia Laguna Resort, Sardinia, Italy},
    month = {May},
    url = {http://leon.bottou.org/papers/usunier-bordes-bottou-2010},
  }

==== Notes =====

This paper finally explains why the 
[[:papers:bordes-bottou-2005|Huller]], 
[[:papers:bordes-ertekin-weston-bottou-2005|LASVM]], and
[[:papers:bordes-2007|LaRank]] algorithms
nearly match their batch counterparts
after performing a single pass on the dataset.