Could someone please point me to the URL for adding Mahout release notes?
On Monday, February 17, 2014 3:27 PM, Ellen Friedman <[email protected]> wrote: Hi Suneel, Thanks for notes. I'm inquiring about status of the notes and update to the website to announce 0.9: Ted has reviewed the release notes - were you waiting for additional input or are they ready to go on the website? Are you the one who updates the site? I've been asked to write a short blog on the release but wanted to wait until the site is updated. Thanks much Ellen On Tue, Feb 11, 2014 at 10:06 AM, Suneel Marthi <[email protected]> wrote: Here's a draft of the Release Notes for Mahout 0.9, Please review the same. > >---------------------------------- > > > >The Apache Mahout PMC is pleased to announce the release of Mahout 0.9. >Mahout's goal is to build scalable machine learning libraries focused >primarily in the areas of collaborative filtering (recommenders), >clustering and classification (known collectively as the "3Cs"), as well as the >necessary infrastructure to support those implementations including, but >not limited to, math packages for statistics, linear algebra and others >as well as Java primitive collections, local and distributed vector and >matrix classes and a variety of integrative code to work with popular >packages like Apache Hadoop, Apache Lucene, Apache HBase, Apache >Cassandra and much more. The 0.9 release is mainly a clean up release in >preparation for an upcoming 1.0 release targeted for first half of 2014, but >there are a few >significant new features, which are highlighted below. > >To get started with Apache Mahout 0.9, download the release artifacts and >signatures at http://www.apache.org/dyn/closer.cgi/mahout or visit the central >Maven repository. > > >As with any release, we wish to thank all of the users and contributors >to Mahout. Please see the CHANGELOG [1] and JIRA Release Notes [2] for >individual credits, as there are too many to list here. > >GETTING STARTED > >In the release package, the examples directory contains several working >examples of the core >functionality available in Mahout. These can be run via scripts in the >examples/bin >directory and will prompt you for more information to help you try things out. >Most examples do not need a Hadoop cluster in order to run. > >RELEASE HIGHLIGHTS > >The highlights of the Apache Mahout 0.9 release include, but are not >limited to the list below. For further information, see the included >CHANGELOG[1] file. > >- MAHOUT-1297: Scala DSL Bindings for Mahout Math Linear Algebra. > See >http://weatheringthrutechdays.blogspot.com/2013/07/scala-dsl-for-mahout-in-core-linear.html >- MAHOUT-1288: Recommenders as a Search. See >https://github.com/pferrel/solr-recommender >- MAHOUT-1364: Upgrade Mahout to Lucene 4.6.1 > >- MAHOUT-1361: Online Algorithm for computing accurate Quantiles using >1-dimensional Clustering > See >https://github.com/tdunning/t-digest/blob/master/docs/theory/t-digest-paper/histo.pdf > for the details. >- MAHOUT-1265: MultiLayer Perceptron (MLP) classifier > This is an early implementation of MLP to solicit user feedback, needs to >be integrated into Mahout’s processing pipeline to work with Mahout’s vectors. > >- Removed Deprecated algorithms as they have been either replaced by better >performing algorithms or lacked user support and maintenance. > >- the usual bug fixes. See [2] for more information on the 0.9 release. > >A total of 113 separate JIRA issues were addressed in this release. > > >The following algorithms that were marked deprecated in 0.8 have been removed >in 0.9: > >- From Clustering: > Switched LDA implementation from using Dirtichlet to Collapsed Variational >Bayes (CVB) > > Meanshift > > MinHash - removed due to poor performance, lack of support and lack of usage > > >- From Classification (both are sequential implementations) > > Winnow - lack of actual usage and support > > Perceptron - lack of actual usage and support > >- Collaborative Filtering > > SlopeOne implementations in org.apache.mahout.cf.taste.hadoop.slopeone and >org.apache.mahout.cf.taste.impl.recommender.slopeone > Distributed pseudo recommender in org.apache.mahout.cf.taste.hadoop.pseudo > TreeClusteringRecommender in org.apache.mahout.cf.taste.impl.recommender > >- Mahout Math > > Hadoop entropy stuff in org.apache.mahout.math.stats.entropy > > >CONTRIBUTING > >Mahout is always looking for contributions focused on the 3Cs. If you are >interested in contributing, please see our contribution page >http://mahout.apache.org/developers/how-to-contribute.html or contact us via >email at [email protected]. > > >As the project moves towards a 1.0 release, the community will be focused on >key algorithms that are proven to scale in production and have seen >wide-spread adoption. > >[1] >http://svn.apache.org/viewvc/mahout/trunk/CHANGELOG?view=markup&pathrev=1563661 >[2] >https://issues.apache.org/jira/browse/MAHOUT-1411?jql=project%20%3D%20MAHOUT%20AND%20fixVersion%20%3D%20%220.9%22 > > > > > > > > > >On Monday, December 23, 2013 7:41 PM, Dmitriy Lyubimov <[email protected]> >wrote: > >On Sun, Dec 22, 2013 at 11:21 AM, Sebastian Schelter < > >[email protected]> wrote: > >> >> > >> > - Mahout Math >> > Lanczos in favour of SSVD >> >> IIRC, we agreed to not remove Lanczos, although it was initially >> deprecated. We should undeprecate it. >> >> >Some folks like Lanczos in Mahout (for reasons not really clear to me, >aside from accuracy when computing svd of a random noise, there are >actually 0 reasons to use Lanczos instead). I agree we don't necessarily >want to cull it out -- but IMO there should be a clear steer posted in >favor of SSVD in the docs/javadocs.
