From bcc48c3a7a72f48d82ccb96fd713512a52436285 Mon Sep 17 00:00:00 2001
From: Patrick Wendell <pwendell@apache.org>
Date: Thu, 11 Sep 2014 23:45:34 +0000
Subject: Adding Spark 1.1.0 release

---
 site/mllib/index.html | 30 ++++++++++++++++--------------
 1 file changed, 16 insertions(+), 14 deletions(-)

(limited to 'site/mllib')

diff --git a/site/mllib/index.html b/site/mllib/index.html
index 4ac0dde7a..6cbedbe8a 100644
--- a/site/mllib/index.html
+++ b/site/mllib/index.html
@@ -104,7 +104,7 @@
         </a>
         <ul class="dropdown-menu">
           <li><a href="/documentation.html">Overview</a></li>
-          <li><a href="/docs/latest/">Latest Release (Spark 1.0.2)</a></li>
+          <li><a href="/docs/latest/">Latest Release (Spark 1.1.0)</a></li>
           <li><a href="/examples.html">Examples</a></li>
         </ul>
       </li>
@@ -132,6 +132,9 @@
       <h5>Latest News</h5>
       <ul class="list-unstyled">
         
+          <li><a href="/news/spark-1-1-0-released.html">Spark 1.1.0 released</a>
+          <span class="small">(Sep 11, 2014)</span></li>
+        
           <li><a href="/news/spark-1-0-2-released.html">Spark 1.0.2 released</a>
           <span class="small">(Aug 05, 2014)</span></li>
         
@@ -141,9 +144,6 @@
           <li><a href="/news/spark-summit-2014-videos-posted.html">Spark Summit 2014 videos posted</a>
           <span class="small">(Jul 18, 2014)</span></li>
         
-          <li><a href="/news/spark-1-0-1-released.html">Spark 1.0.1 released</a>
-          <span class="small">(Jul 11, 2014)</span></li>
-        
       </ul>
       <p class="small" style="text-align: right;"><a href="/news/index.html">Archive</a></p>
     </div>
@@ -191,9 +191,9 @@
         points = spark.textFile(<span class="string">"hdfs://..."</span>)<br />
         &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;.<span class="sparkop">map</span>(<span class="closure">parsePoint</span>)<br />
         <br />
-        model = KMeans.<span class="sparkop">train</span>(points)
+        model = KMeans.<span class="sparkop">train</span>(points, k=10)
       </div>
-      <div class="caption">Calling MLlib in Scala</div>
+      <div class="caption">Calling MLlib in Python</div>
     </div>
   </div>
 </div>
@@ -247,16 +247,18 @@
   <div class="col-md-4 col-padded">
     <h3>Algorithms</h3>
     <p>
-      MLlib 0.9 contains the following algorithms:
+      MLlib 1.1 contains the following algorithms:
     </p>
     <ul class="list-narrow">
-      <li>K-means clustering with <a href="http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf">K-means|| initialization</a>.</li>
-      <li>L<sub>1</sub>- and L<sub>2</sub>-regularized <a href="http://en.wikipedia.org/wiki/Linear_regression">linear regression</a>.</li>
-      <li>L<sub>1</sub>- and L<sub>2</sub>-regularized <a href="http://en.wikipedia.org/wiki/Logistic_regression">logistic regression</a>.</li>
-      <li><a href="http://www.hpl.hp.com/personal/Robert_Schreiber/papers/2008%20AAIM%20Netflix/netflix_aaim08(submitted).pdf">Alternating least squares</a> collaborative filtering, with explicit
-      ratings or <a href="http://www2.research.att.com/~yifanhu/PUB/cf.pdf">implicit feedback</a>.</li>
-      <li><a href="http://en.wikipedia.org/wiki/Naive_Bayes_classifier">Naive Bayes</a> multinomial classification.</li>
-      <li>Stochastic gradient descent.</li>
+      <li>linear SVM and logistic regression</li>
+      <li>classification and regression tree</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>
+      <li>multinomial naive Bayes</li>
+      <li>basic statistics</li>
+      <li>feature transformations</li>
     </ul>
     <p>Refer to the <a href="/docs/latest/mllib-guide.html">MLlib guide</a> for usage examples.</p>
   </div>
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