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Commit a576b20f authored by Davis King's avatar Davis King
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Added a flow chart diagram to help users pick which machine learning tool to 

use.
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docs/docs/ml.xml
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...@@ -9,25 +9,9 @@ ...@@ -9,25 +9,9 @@
<body> <body>
<br/><br/> <br/><br/>
<a href="ml_guide.svg"><img src="ml_guide.svg" width="100%"/></a>
<p> <p>
This page documents all the machine learning algorithms present in A major design goal of this portion of the library is to provide a highly modular and
the library. In particular, there are algorithms for performing
classification, regression, clustering, sequence labeling, assignment learning,
rank learning, graph segmentation, object detection, anomaly detection,
and feature ranking, as well as algorithms for doing more
specialized computations.
</p>
<p>
A good tutorial and introduction to the general concepts used by most of the
objects in this part of the library can be found in the <a href="svm_ex.cpp.html">svm example</a> program.
After reading this example another good one to consult would be the <a href="model_selection_ex.cpp.html">model selection</a>
example program. Finally, if you came here looking for a binary classification or regression tool then I would
try the <a href="#krr_trainer">krr_trainer</a> first as it is generally the easiest method to use.
</p>
<p>
The major design goal of this portion of the library is to provide a highly modular and
simple architecture for dealing with kernel algorithms. Towards this end, dlib takes a generic simple architecture for dealing with kernel algorithms. Towards this end, dlib takes a generic
programming approach using C++ templates. In particular, each algorithm is parameterized programming approach using C++ templates. In particular, each algorithm is parameterized
to allow a user to supply either one of the predefined dlib kernels (e.g. <a to allow a user to supply either one of the predefined dlib kernels (e.g. <a
...@@ -41,7 +25,6 @@ ...@@ -41,7 +25,6 @@
</p> </p>
<br/>
<h3>Paper Describing dlib Machine Learning</h3> <h3>Paper Describing dlib Machine Learning</h3>
<pre> <pre>
Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09a.pdf">Dlib-ml: A Machine Learning Toolkit</a>. Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09a.pdf">Dlib-ml: A Machine Learning Toolkit</a>.
...@@ -470,6 +453,11 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09 ...@@ -470,6 +453,11 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
regression algorithm. You give it samples (x,y) and it learns the function regression algorithm. You give it samples (x,y) and it learns the function
f(x) == y. For a detailed description of the algorithm read the above paper. f(x) == y. For a detailed description of the algorithm read the above paper.
</p> </p>
<p>
Note that if you want to use the linear kernel then you would
be better off using the <a href="#rls">rls</a> object as it
is optimized for this case.
</p>
</description> </description>
<examples> <examples>
...@@ -554,6 +542,10 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09 ...@@ -554,6 +542,10 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<description> <description>
This is an implementation of a kernelized k-means clustering algorithm. This is an implementation of a kernelized k-means clustering algorithm.
It performs k-means clustering by using the <a href="#kcentroid">kcentroid</a> object. It performs k-means clustering by using the <a href="#kcentroid">kcentroid</a> object.
<p>
If you want to use the linear kernel (i.e. do a normal k-means clustering) then you
should use the <a href="#find_clusters_using_kmeans">find_clusters_using_kmeans</a> routine.
</p>
</description> </description>
<examples> <examples>
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docs/makedocs
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cp docs/*.gif docs/web cp docs/*.gif docs/web
cp docs/*.gif docs/chm/docs cp docs/*.gif docs/chm/docs
cp docs/ml_guide.svg docs/web
cp docs/ml_guide.svg docs/chm/docs
cp -r docs/guipics docs/web cp -r docs/guipics docs/web
cp -r docs/guipics docs/chm/docs cp -r docs/guipics docs/chm/docs
cp docs/*.html docs/web cp docs/*.html docs/web
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