You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mahout.apache.org by bu...@apache.org on 2013/11/20 12:44:47 UTC
svn commit: r887342 - in /websites/staging/mahout/trunk/content: ./
users/basics/algorithms.html
Author: buildbot
Date: Wed Nov 20 11:44:47 2013
New Revision: 887342
Log:
Staging update by buildbot for mahout
Modified:
websites/staging/mahout/trunk/content/ (props changed)
websites/staging/mahout/trunk/content/users/basics/algorithms.html
Propchange: websites/staging/mahout/trunk/content/
------------------------------------------------------------------------------
--- cms:source-revision (original)
+++ cms:source-revision Wed Nov 20 11:44:47 2013
@@ -1 +1 @@
-1543780
+1543790
Modified: websites/staging/mahout/trunk/content/users/basics/algorithms.html
==============================================================================
--- websites/staging/mahout/trunk/content/users/basics/algorithms.html (original)
+++ websites/staging/mahout/trunk/content/users/basics/algorithms.html Wed Nov 20 11:44:47 2013
@@ -386,7 +386,7 @@
<p>This section contains links to information, examples, use cases, etc. for
the various algorithms we support. Click the individual links
to learn more. The algorithms are grouped by use case.</p>
-<p>For Papers, videos and books related to machine learning in general, see <a href="machine-learning-resources.html">Machine Learning Resources</a></p>
+<p>For Papers, videos and books related to machine learning in general, see <a href="../general/reference-reading.html">Machine Learning Resources</a></p>
<h2 id="general-advise">General advise</h2>
<p>The main goal of Apache Mahout is to be useful to practitioners. This means implementations should be easy to
use from within Java applications. It should be close to trivial to deploy the trained models. Scaling to include
@@ -415,7 +415,7 @@ implementation) please visit the followi
<li><a href="../classification/bayesian.html">Naive Bayes/ Complementary Naive Bayes</a> - training runs on Hadoop</li>
<li><a href="../classification/random-forests.html">Random Forests</a>
(<a href="http://issues.apache.org/jira/browse/MAHOUT-122">MAHOUT-122</a>, - training is done in Hadoop
- <a href="http://issues.apache.org/jira/browse/MAHOUT-140">MAHOUT-140</a>, <a href="http://issues.apache.org/jira/browse/MAHOUT-145">MAHOUT-145</a></li>
+ <a href="http://issues.apache.org/jira/browse/MAHOUT-140">MAHOUT-140</a>, <a href="http://issues.apache.org/jira/browse/MAHOUT-145">MAHOUT-145</a>)</li>
<li><a href="../stuff/hidden-markov-models.html">Hidden Markov Models</a> (see MAHOUT-627, MAHOUT-396, MAHOUT-734) - training is done in
Map-Reduce</li>
</ul>
@@ -424,15 +424,14 @@ Map-Reduce</li>
<li><a href="../classification/support-vector-machines.html">Support Vector Machines</a> (see <a href="http://issues.apache.org/jira/browse/MAHOUT-14">MAHOUT-14</a>
, <a href="http://issues.apache.org/jira/browse/MAHOUT-232">MAHOUT-232</a>
and <a href="https://issues.apache.org/jira/browse/MAHOUT-334">MAHOUT-334</a> </li>
-<li><a href="../classification/perceptron-and-winnow.html">Perceptron and Winnow</a>
+<li><a href="../stuff/perceptron-and-winnow.html">Perceptron and Winnow</a>
(see <a href="http://issues.apache.org/jira/browse/MAHOUT-85">MAHOUT-85</a>)</li>
<li><a href="../classification/neural-network.html">Neural Network</a>
(see <a href="http://issues.apache.org/jira/browse/MAHOUT-228">MAHOUT-228</a>)</li>
<li><a href="../classification/restricted-boltzmann-machines.html">Restricted Boltzmann Machines</a>
(see <a href="http://issues.apache.org/jira/browse/MAHOUT-375">MAHOUT-375</a>)</li>
-<li><a href="../classification/online-passive-aggressive.html">Online Passive Aggressive</a>
- (see <a href="http://issues.apache.org/jira/browse/MAHOUT-702">MAHOUT-702</a></li>
-<li><a href="../classification/boosting.html">Boosting</a> (see <a href="https://issues.apache.org/jira/browse/MAHOUT-716">MAHOUT-716</a>)</li>
+<li>Online Passive Aggressive (see <a href="http://issues.apache.org/jira/browse/MAHOUT-702">MAHOUT-702</a></li>
+<li>Boosting (see <a href="https://issues.apache.org/jira/browse/MAHOUT-716">MAHOUT-716</a>)</li>
</ul>
<p><a name="Algorithms-Clustering"></a></p>
<h2 id="clustering">Clustering</h2>
@@ -452,18 +451,15 @@ Map-Reduce</li>
(<a href="http://issues.apache.org/jira/browse/MAHOUT-30">MAHOUT-30</a> - runs on Hadoop</li>
<li><a href="../clustering/latent-dirichlet-allocation.html">Latent Dirichlet Allocation</a>
(<a href="http://issues.apache.org/jira/browse/MAHOUT-123">MAHOUT-123</a>) - runs on Hadoop</li>
-<li><a href="../clustering/minhash-clustering.html">Minhash Clustering</a>
- (<a href="https://issues.apache.org/jira/browse/MAHOUT-344">MAHOUT-344</a>) - runs on Hadoop</li>
+<li>Minhash Clustering (<a href="https://issues.apache.org/jira/browse/MAHOUT-344">MAHOUT-344</a>) - runs on Hadoop</li>
<li>kMeans++ streaming clustering - documentation missing</li>
</ul>
<p>Deprecated or drafts only:</p>
<ul>
<li><a href="../clustering/hierarchical-clustering.html">Hierarchical Clustering</a>
- (<a href="http://issues.apache.org/jira/browse/MAHOUT-19">MAHOUT-19</a>)</li>
+ (<a href="http://issues.apache.org/jira/browse/MAHOUT-19">MAHOUT-19</a>, <a href="https://issues.apache.org/jira/browse/MAHOUT-843">MAHOUT-843</a>)</li>
<li><a href="../clustering/spectral-clustering.html">Spectral Clustering</a>
(<a href="https://issues.apache.org/jira/browse/MAHOUT-363">MAHOUT-363</a>)</li>
-<li><a href="../clustering/top-down-clustering.html">Top Down Clustering</a>
- (<a href="https://issues.apache.org/jira/browse/MAHOUT-843">MAHOUT-843</a>)</li>
</ul>
<p><a name="Algorithms-Dimensionreduction"></a></p>
<h2 id="dimension-reduction">Dimension reduction</h2>
@@ -471,42 +467,38 @@ Map-Reduce</li>
<ul>
<li><a href="dimensional-reduction.html">Singular Value Decomposition and other Dimension Reduction Techniques</a>
(available since 0.3)</li>
-<li><a href="stochastic-singular-value-decomposition.html">Stochastic Singular Value Decomposition with PCA workflow</a>
- (PCA workflow now integrated)</li>
+<li>Stochastic Singular Value Decomposition with PCA workflow</li>
</ul>
<p>Deprecated or drafts only:</p>
<ul>
<li><a href="principal-components-analysis.html">Principal Components Analysis</a>
(PCA) </li>
-<li><a href="independent-component-analysis.html">Independent Component Analysis</a></li>
<li><a href="gaussian-discriminative-analysis.html">Gaussian Discriminative Analysis</a>
(GDA) </li>
</ul>
<p><a name="Algorithms-EvolutionaryAlgorithms"></a></p>
<h2 id="evolutionary-algorithms">Evolutionary Algorithms</h2>
-<ul>
-<li>NOTE: Watchmaker support has been removed as of 0.7</li>
-</ul>
-<p>see also: <a href="http://issues.apache.org/jira/browse/MAHOUT-56">MAHOUT-56 (integrated)</a></p>
+<p>NOTE: Watchmaker support has been removed as of 0.7</p>
+<p>see also: <a href="http://issues.apache.org/jira/browse/MAHOUT-56">MAHOUT-56</a></p>
<p>You will find here information, examples, use cases, etc. related to
Evolutionary Algorithms.</p>
-<p>Introductions and Tutorials:
-<em> <a href="http://www.geatbx.com/docu/algindex.html">Evolutionary Algorithms Introduction</a>
-</em> <a href="mahout.ga.tutorial.html">How to distribute the fitness evaluation using Mahout.GA</a></p>
-<p>Examples:
-<em> <a href="traveling-salesman.html">Traveling Salesman</a>
-</em> <a href="class-discovery.html">Class Discovery</a></p>
+<p>Introductions and Tutorials:</p>
+<ul>
+<li><a href="http://www.geatbx.com/docu/algindex.html">Evolutionary Algorithms Introduction</a></li>
+<li><a href="mahout.ga.tutorial.html">How to distribute the fitness evaluation using Mahout.GA</a></li>
+</ul>
+<p>Example: <a href="../stuff/traveling-salesman.html">Traveling Salesman</a></p>
<p><a name="Algorithms-Recommenders/CollaborativeFiltering"></a></p>
<h2 id="recommenders-collaborative-filtering">Recommenders / Collaborative Filtering</h2>
<p>Mahout contains both simple non-distributed recommender implementations and
distributed Hadoop-based recommenders.</p>
<ul>
-<li><a href="recommender-first-timer-faq.html">First-timer FAQ</a></li>
-<li><a href="recommender-documentation.html">Non-distributed recommenders ("Taste")</a></li>
-<li><a href="itembased-collaborative-filtering.html">Distributed Item-Based Collaborative Filtering</a></li>
-<li><a href="collaborative-filtering-with-als-wr.html">Collaborative Filtering using a parallel matrix factorization</a></li>
+<li><a href="../recommender/recommender-first-timer-faq.html">First-timer FAQ</a></li>
+<li><a href="../recommender/recommender-documentation.html">Non-distributed recommenders ("Taste")</a></li>
+<li><a href="../recommender/itembased-collaborative-filtering.html">Distributed Item-Based Collaborative Filtering</a></li>
+<li>Collaborative Filtering using a parallel matrix factorization
+<a name="Algorithms-Other"></a></li>
</ul>
-<p><a name="Algorithms-Other"></a></p>
<h2 id="other">Other</h2>
<p>Fullly supported:</p>
<ul>