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| author | Matei Alexandru Zaharia <matei@apache.org> | 2015-01-22 00:27:22 +0000 |
|---|---|---|
| committer | Matei Alexandru Zaharia <matei@apache.org> | 2015-01-22 00:27:22 +0000 |
| commit | d24b0eb9d14c1d389ea02b90015f9d1f77cd96f0 (patch) | |
| tree | eeab780a03576eade0027097adef83885d20f700 /site/mllib | |
| parent | d4b39448d40aac6a9d092a84661db09755bcfeb0 (diff) | |
| download | spark-website-d24b0eb9d14c1d389ea02b90015f9d1f77cd96f0.tar.gz spark-website-d24b0eb9d14c1d389ea02b90015f9d1f77cd96f0.tar.bz2 spark-website-d24b0eb9d14c1d389ea02b90015f9d1f77cd96f0.zip | |
Add Summit East news item
Diffstat (limited to 'site/mllib')
| -rw-r--r-- | site/mllib/index.html | 11 |
1 files changed, 5 insertions, 6 deletions
diff --git a/site/mllib/index.html b/site/mllib/index.html index 72f2adcf2..d74d3cde5 100644 --- a/site/mllib/index.html +++ b/site/mllib/index.html @@ -136,6 +136,9 @@ <h5>Latest News</h5> <ul class="list-unstyled"> + <li><a href="/news/spark-summit-east-agenda-posted.html">Spark Summit East agenda posted, CFP open for West</a> + <span class="small">(Jan 21, 2015)</span></li> + <li><a href="/news/spark-1-2-0-released.html">Spark 1.2.0 released</a> <span class="small">(Dec 18, 2014)</span></li> @@ -145,9 +148,6 @@ <li><a href="/news/registration-open-for-spark-summit-east.html">Registration open for Spark Summit East 2015</a> <span class="small">(Nov 26, 2014)</span></li> - <li><a href="/news/spark-wins-daytona-gray-sort-100tb-benchmark.html">Spark wins Daytona Gray Sort 100TB Benchmark</a> - <span class="small">(Nov 05, 2014)</span></li> - </ul> <p class="small" style="text-align: right;"><a href="/news/index.html">Archive</a></p> </div> @@ -248,13 +248,12 @@ <div class="col-md-4 col-padded"> <h3>Algorithms</h3> <p> - MLlib 1.2 contains the following algorithms: + MLlib 1.1 contains the following algorithms: </p> <ul class="list-narrow"> <li>linear SVM and logistic regression</li> <li>classification and regression tree</li> - <li>random forests and gradient-boosted trees</li> - <li>k-means clustering and streaming k-means</li> + <li>k-means clustering</li> <li>recommendation via alternating least squares</li> <li>singular value decomposition</li> <li>linear regression with L<sub>1</sub>- and L<sub>2</sub>-regularization</li> |