Skip to content

D
dlib
Commit a576b20f authored by Davis King's avatar Davis King
Browse files
Options

Added a flow chart diagram to help users pick which machine learning tool to 

use.
parent 31eca31e
Hide whitespace changes
Inline Side-by-side
Showing with 13 additions and 19 deletions
docs/docs/ml.xml
View file @ a576b20f
......@@ -9,25 +9,9 @@
<body>
<br/><br/>
<p>
This page documents all the machine learning algorithms present in
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>
<a href="ml_guide.svg"><img src="ml_guide.svg" width="100%"/></a>
<p>
The major design goal of this portion of the library is to provide a highly modular and
A 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
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
......@@ -41,7 +25,6 @@
</p>
<br/>
<h3>Paper Describing dlib Machine Learning</h3>
<pre>
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
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.
</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>
<examples>
......@@ -554,6 +542,10 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<description>
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.
<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>
<examples>
......
docs/docs/ml_guide.svg 0 → 100644
View file @ a576b20f
This source diff could not be displayed because it is too large. You can view the blob instead.
docs/makedocs
View file @ a576b20f
......@@ -152,6 +152,8 @@ makedocs ()
cp docs/*.gif docs/web
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/chm/docs
cp docs/*.html docs/web
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment