Some interesting looking references there, will take a look - thanks!
Regards,
Nigel Legg
07914 740972
http://www.trevanianlegg.co.uk
http://twitter.com/nigellegg
http://uk.linkedin.com/in/nigellegg
On 19 August 2013 17:46, Peter Prettenhofer <peter.prettenho...@gmail.com>wrote:
> Hi Yogesh,
>
> the work by John Blitzer that I mentioned used the second approach -- its
> described here:
>
> Blitzer, J., Dredze, M., Pereira, F., Jun. 2007. Biographies, bollywood,
> boom-boxes and blenders: Domain adaptation for sentiment classification.
> In: Proceedings of ACL, Prague, Czech Republic, pp. 440-447.
> URL http://www.aclweb.org/anthology-new/P/P07/P07-1056.bib
>
> Blitzer, J., Mcdonald, R., Pereira, F., Jul. 2006. Domain adaptation with
> structural correspondence learning. In: Proceedings of the 2006 Conference
> on EMNLP, Sydney, Australia, pp. 120-128.
> URL http://www.cis.upenn.edu/\~{}blitzer/papers/emnlp06.pdf
>
> he and Shai B. David did also some theoretical work that introduces the
> distance I was talking about:
>
> David, S. B., Blitzer, J., Crammer, K., Pereira, F., 2006. Analysis of
> representations for domain adaptation. In: Schölkopf, B., Platt, J. C.,
> Hoffman, T., Schölkopf, B., Platt, J. C., Hoffman, T. (Eds.), NIPS. MIT
> Press, pp. 137-144.
> URL http://dblp.uni-trier.de/rec/bibtex/conf/nips/Ben-DavidBCP06
>
> David, S. B., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., Vaughan,
> J. W., May 2010. A theory of learning from different domains. Mach. Learn.
> 79 (1), 151-175.
> URL http://dx.doi.org/10.1007/s10994-009-5152-4
>
> I strongly recommend the book on Dataset Shift in Machine Learning edited
> by Joaquin Quiñonero-Candela, Masashi Sugiyama, Anton Schwaighofer and Neil
> D. Lawrence .
> http://mitpress.mit.edu/books/dataset-shift-machine-learning
> It contains articles from the most active researchers in the field - the
> book is the result of a NIPS workshop that has been partially taped
> (available on videolectures.net).
>
> Other survey that might be helpful are:
>
> Pan, S. J., Yang, Q., Oct. 2010. A survey on transfer learning. IEEE
> Transactions on Knowledge and Data Engineering 22 (10), 1345-1359.
> URL http://dx.doi.org/10.1109/tkde.2009.191
>
> Jing Jiang, A Literature Survey on Domain Adaptation of Statistical
> Classifiers,
> http://sifaka.cs.uiuc.edu/jiang4/domain_adaptation/survey/da_survey.pdf
>
> HTH,
> Peter
>
>
> 2013/8/19 Yogesh Karpate <yogeshkarp...@gmail.com>
>
>> Hi Folks,
>> Thanks a lot for suggesting me good references!
>> @ Peter : You can send me the more ref.
>> @ Gael : WIsh you a speedy recovery!
>> @ Olivier : Thanks a lot for listening my problem quitely and asking for
>> clarifications.
>> Next time and onwards I will try to be more specific explaining the
>> problem!
>> I will get back to you guys once I finish experiments.
>>
>>
>> On Mon, Aug 19, 2013 at 9:12 AM, Gael Varoquaux <
>> gael.varoqu...@normalesup.org> wrote:
>>
>>> Hi list,
>>>
>>> Coming back from travel, with a slight elbow injury that makes typing
>>> difficult...
>>>
>>> Anyhow, I just wanted to stress that a lot of good advice has been
>>> put forward in the discussion so far, and that, when we find time, I
>>> think that a subsection of the docs dealing on class-imbalance, covariate
>>> shift, domain adaption, zero-shoot learning... you name it, would be
>>> useful, alongside with learning on text, or advices for big data.
>>>
>>> G
>>>
>>>
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>>
>>
>>
>> --
>> Cheers!
>> Yogesh Karpate
>>
>>
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>>
>
>
> --
> Peter Prettenhofer
>
>
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