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Posted to commits@spark.apache.org by me...@apache.org on 2014/09/01 07:06:58 UTC
svn commit: r1621673 - in /spark: mllib/index.md site/mllib/index.html
Author: meng
Date: Mon Sep 1 05:06:58 2014
New Revision: 1621673
URL: http://svn.apache.org/r1621673
Log:
update mllib webpage
Modified:
spark/mllib/index.md
spark/site/mllib/index.html
Modified: spark/mllib/index.md
URL: http://svn.apache.org/viewvc/spark/mllib/index.md?rev=1621673&r1=1621672&r2=1621673&view=diff
==============================================================================
--- spark/mllib/index.md (original)
+++ spark/mllib/index.md Mon Sep 1 05:06:58 2014
@@ -29,9 +29,9 @@ subproject: MLlib
points = spark.textFile(<span class="string">"hdfs://..."</span>)<br/>
.<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>
@@ -82,16 +82,18 @@ subproject: MLlib
<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="{{site.url}}docs/latest/mllib-guide.html">MLlib guide</a> for usage examples.</p>
</div>
Modified: spark/site/mllib/index.html
URL: http://svn.apache.org/viewvc/spark/site/mllib/index.html?rev=1621673&r1=1621672&r2=1621673&view=diff
==============================================================================
--- spark/site/mllib/index.html (original)
+++ spark/site/mllib/index.html Mon Sep 1 05:06:58 2014
@@ -186,9 +186,9 @@
points = spark.textFile(<span class="string">"hdfs://..."</span>)<br />
.<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>
@@ -242,16 +242,18 @@
<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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