Thanks for the pointers Peter. I'm doing an unrelated project on covariate shift, and this will be really useful.
Lee. On Mon, Aug 19, 2013 at 12:46 PM, 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 >>> >>> >>> ------------------------------------------------------------------------------ >>> Get 100% visibility into Java/.NET code with AppDynamics Lite! >>> It's a free troubleshooting tool designed for production. >>> Get down to code-level detail for bottlenecks, with <2% overhead. >>> Download for free and get started troubleshooting in minutes. >>> >>> http://pubads.g.doubleclick.net/gampad/clk?id=48897031&iu=/4140/ostg.clktrk >>> _______________________________________________ >>> Scikit-learn-general mailing list >>> Scikit-learn-general@lists.sourceforge.net >>> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> >> >> >> >> -- >> Cheers! >> Yogesh Karpate >> >> >> ------------------------------------------------------------------------------ >> Get 100% visibility into Java/.NET code with AppDynamics Lite! >> It's a free troubleshooting tool designed for production. >> Get down to code-level detail for bottlenecks, with <2% overhead. >> Download for free and get started troubleshooting in minutes. >> >> http://pubads.g.doubleclick.net/gampad/clk?id=48897031&iu=/4140/ostg.clktrk >> _______________________________________________ >> Scikit-learn-general mailing list >> Scikit-learn-general@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >> > > > > -- > Peter Prettenhofer > > ------------------------------------------------------------------------------ > Introducing Performance Central, a new site from SourceForge and > AppDynamics. 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