Just tried this for some hot trends in forum managements. Was pretty impressive.
I will try this more deeply and if possible integrate in my product. Thanks for the awesome work. Nitin On Fri, Oct 11, 2013 at 12:58 PM, Makoto YUI <yuin...@gmail.com> wrote: > Hi, > > I added support for the-state-of-the-art classifiers (those are not yet > supported in Mahout) and Hivemall's cute(!?) logo as well in Hivemall > 0.1-rc3. > > Newly supported classifiers include > - Confidence Weighted (CW) > - Adaptive Regularization of Weight Vectors (AROW) > - Soft Confidence Weighted (SCW1, SCW2) > > Those classifiers are much smart comparing to the standard SGD-based or > passive aggressive classifiers. Please check it out by yourself. > > Thanks, > Makoto > > > (2013/10/11 4:28), Clark Yang (杨卓荦) wrote: > >> I looks really cool, I think I will try it on. >> >> Cheers, >> Zhuoluo (Clark) Yang >> >> >> 2013/10/5 Makoto YUI <yuin...@gmail.com <mailto:yuin...@gmail.com>> >> >> >> Hi Edward, >> >> Thank you for your interst. >> >> Hivemall project does not have a plan to have a specific mailing >> list, I will answer following questions/comments on twitter or >> through Github issues (with a question label). >> >> BTW, I just added a CTR (Click-Through-Rate) prediction example that >> is >> provided by a commercial search engine provider for the KDDCup 2012 >> track 2. >> https://github.com/myui/__**hivemall/wiki/KDDCup-2012-__** >> track-2-CTR-prediction-dataset<https://github.com/myui/__hivemall/wiki/KDDCup-2012-__track-2-CTR-prediction-dataset> >> >> <https://github.com/myui/**hivemall/wiki/KDDCup-2012-** >> track-2-CTR-prediction-dataset<https://github.com/myui/hivemall/wiki/KDDCup-2012-track-2-CTR-prediction-dataset> >> **> >> >> I guess many of you working on ad CTR/CVR predictions. This example >> might be some help understanding how to do it only within Hive. >> >> Thanks, >> Makoto @myui >> >> >> (2013/10/04 23:02), Edward Capriolo wrote: >> >> Looks cool im already starting to play with it. >> >> On Friday, October 4, 2013, Makoto Yui <yuin...@gmail.com >> <mailto:yuin...@gmail.com> >> <mailto:yuin...@gmail.com <mailto:yuin...@gmail.com>>> wrote: >> > Hi Dean, >> > >> > Thank you for your interest in Hivemall. >> > >> > Twitter's paper actually influenced me in developing >> Hivemall and I >> > initially implemented such functionality as Pig UDFs. >> > >> > Though my Pig ML library is not released, you can find a >> similar >> > attempt for Pig in >> > >> https://github.com/y-tag/java-**__pig-MyUDFs<https://github.com/y-tag/java-__pig-MyUDFs> >> >> >> <https://github.com/y-tag/**java-pig-MyUDFs<https://github.com/y-tag/java-pig-MyUDFs> >> > >> > >> > Thanks, >> > Makoto >> > >> > 2013/10/3 Dean Wampler <deanwamp...@gmail.com >> <mailto:deanwamp...@gmail.com> >> <mailto:deanwamp...@gmail.com <mailto:deanwamp...@gmail.com>** >> >__>: >> >> >> >> This is great news! I know that Twitter has done something >> similar >> with UDFs >> >> for Pig, as described in this paper: >> >> >> http://www.umiacs.umd.edu/~__**jimmylin/publications/Lin___** >> Kolcz_SIGMOD2012.pdf<http://www.umiacs.umd.edu/~__jimmylin/publications/Lin___Kolcz_SIGMOD2012.pdf> >> <http://www.umiacs.umd.edu/%**7Ejimmylin/publications/Lin_** >> Kolcz_SIGMOD2012.pdf<http://www.umiacs.umd.edu/%7Ejimmylin/publications/Lin_Kolcz_SIGMOD2012.pdf> >> > >> <http://www.umiacs.umd.edu/%__**7Ejimmylin/publications/Lin___** >> Kolcz_SIGMOD2012.pdf >> >> <http://www.umiacs.umd.edu/%**7Ejimmylin/publications/Lin_** >> Kolcz_SIGMOD2012.pdf<http://www.umiacs.umd.edu/%7Ejimmylin/publications/Lin_Kolcz_SIGMOD2012.pdf> >> >> >> >> >> >> >> I'm glad to see the same thing start with Hive. >> >> >> >> Dean >> >> >> >> >> >> On Wed, Oct 2, 2013 at 10:21 AM, Makoto YUI >> <yuin...@gmail.com <mailto:yuin...@gmail.com> >> <mailto:yuin...@gmail.com <mailto:yuin...@gmail.com>>> wrote: >> >>> >> >>> Hello all, >> >>> >> >>> My employer, AIST, has given the thumbs up to open source >> our machine >> >>> learning library, named Hivemall. >> >>> >> >>> Hivemall is a scalable machine learning library running on >> Hive/Hadoop, >> >>> licensed under the LGPL 2.1. >> >>> >> >>> >> https://github.com/myui/__**hivemall<https://github.com/myui/__hivemall> >> >> <https://github.com/myui/**hivemall<https://github.com/myui/hivemall> >> > >> >>> >> >>> Hivemall provides machine learning functionality as well >> as feature >> >>> engineering functions through UDFs/UDAFs/UDTFs of Hive. It >> is designed >> >>> to be scalable to the number of training instances as well >> as the >> number >> >>> of training features. >> >>> >> >>> Hivemall is very easy to use as every machine learning >> step is done >> >>> within HiveQL. >> >>> >> >>> -- Installation is just as follows: >> >>> add jar /tmp/hivemall.jar; >> >>> source /tmp/define-all.hive; >> >>> >> >>> -- Logistic regression is performed by a query. >> >>> SELECT >> >>> feature, >> >>> avg(weight) as weight >> >>> FROM >> >>> (SELECT logress(features,label) as (feature,weight) FROM >> >>> training_features) t >> >>> GROUP BY feature; >> >>> >> >>> You can find detailed examples on our wiki pages. >> >>> >> https://github.com/myui/__**hivemall/wiki/_pages<https://github.com/myui/__hivemall/wiki/_pages> >> >> >> <https://github.com/myui/**hivemall/wiki/_pages<https://github.com/myui/hivemall/wiki/_pages> >> > >> >>> >> >>> Though we consider that Hivemall is much easier to use and >> more >> scalable >> >>> than Mahout for classification/regression tasks, please >> check it by >> >>> yourself. If you have a Hive environment, you can evaluate >> Hivemall >> >>> within 5 minutes or so. >> >>> >> >>> Hope you enjoy the release! Feedback (and pull request) is >> always >> welcome. >> >>> >> >>> Thank you, >> >>> Makoto >> >> >> >> >> >> >> >> >> >> -- >> >> Dean Wampler, Ph.D. >> >> @deanwampler >> >> http://polyglotprogramming.com >> > >> >> >> >> > -- Nitin Pawar
