Thanks I just saw. I'll give it a read tomorrow. Andy
On 03/23/2015 08:09 PM, Wei Xue wrote:
Hi Andreas, I have submitted my updated proposal as well. Thanks! Wei Xue On Mon, Mar 16, 2015 at 4:36 PM, Andreas Mueller <t3k...@gmail.com <mailto:t3k...@gmail.com>> wrote:Hi Wei Xue. I am also not very convinced by the core-set approach. I'd rather focus on improving the API and fixing issues in the VBGMM and DPGMM. I was hoping that Murphy's book has some more details on DPGMM, but I didn't find any yet. He doesn't seem to talk about variational inference in Dirichlet processes. So far I think your proposal looks solid. It would be great if you could work on some pull requests to support your application. Best, Andy On 03/16/2015 04:23 PM, Wei Xue wrote:Hi groups, I am a PhD student in Florida International University, US. I am interested in the topic improving GMM. I draft a proposal for this topic. https://github.com/xuewei4d/scikit-learn/wiki/GSoC-2015-Proposal:-Improve-GMM Here are some questions I would like to discuss. 1. -1 for coreset. The paper(http://las.ethz.ch/files/feldman11scalable-long.pdf) is new and its citations less than 15. The application situations are on clusters, streaming data, which is (I think) is rare for scikit-learn. 2. Currently, I have gone over the Approximation Inference chapter in PRML (Bishop's machine learning book) and Blei's 2006 paper. But I have not dig much into the code, so I don't have a detailed reimplement plan yet. Do I need to add more details into the 'Theory and Implementation' part of the proposal? 3. Any feedback is welcome. Thanks, Wei Xue ------------------------------------------------------------------------------ Dive into the World of Parallel Programming The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now.http://goparallel.sourceforge.net/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net <mailto:Scikit-learn-general@lists.sourceforge.net> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general------------------------------------------------------------------------------ Dive into the World of Parallel Programming The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now. http://goparallel.sourceforge.net/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net <mailto:Scikit-learn-general@lists.sourceforge.net> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general ------------------------------------------------------------------------------ Dive into the World of Parallel Programming The Go Parallel Website, sponsored by Intel and developed in partnership with Slashdot Media, is your hub for all things parallel software development, from weekly thought leadership blogs to news, videos, case studies, tutorials and more. Take a look and join the conversation now. http://goparallel.sourceforge.net/ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
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