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Commit d0f66410 authored by Davis King's avatar Davis King
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updated the docs 

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docs/docs/algorithms.xml
View file @ d0f66410
......@@ -121,6 +121,7 @@
<item>polynomial_kernel</item>
<item>sigmoid_kernel</item>
<item>linear_kernel</item>
<item>offset_kernel</item>
</sub>
</item>
<item nolink="true">
......@@ -150,6 +151,10 @@
<item>reduced</item>
<item>reduced_decision_function_trainer2</item>
<item>reduced2</item>
<item>batch</item>
<item>verbose_batch</item>
<item>batch_trainer</item>
<item>svm_pegasos</item>
</sub>
</item>
<item>randomize_samples</item>
......@@ -858,6 +863,30 @@
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_pegasos</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This object implements an online algorithm for training a support
vector machine for solving binary classification problems.
<p>
The implementation of the Pegasos algorithm used by this object is based
on the following excellent paper:
<blockquote>
Pegasos: Primal estimated sub-gradient solver for SVM (2007)
by Yoram Singer, Nathan Srebro
In ICML
</blockquote>
</p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
......@@ -1173,6 +1202,19 @@
</component>
<!-- ************************************************************************* -->
<component>
<name>offset_kernel</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/kernel_abstract.h</spec_file>
<description>
This object represents a kernel with a fixed value offset
added to it.
</description>
</component>
<!-- ************************************************************************* -->
<component>
......@@ -1277,6 +1319,51 @@
</component>
<!-- ************************************************************************* -->
<component>
<name>batch</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#batch_trainer">batch_trainer</a> objects.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>verbose_batch</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This is a convenience function for creating
<a href="#batch_trainer">batch_trainer</a> objects. This function
generates a batch_trainer that will print status messages to standard
output so that you can observe the progress of a training algorithm.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>batch_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/pegasos_abstract.h</spec_file>
<description>
This is a batch trainer object that is meant to wrap other online trainer objects
that create <a href="#decision_function">decision_functions</a>. It
turns an online learning object such as the <a href="#svm_pegasos">svm_pegasos</a>
object into a batch learning object. This allows you to use objects like
svm_pegasos with functions, such as <a href="#cross_validate_trainer">cross_validate_trainer</a>,
that expect batch mode training objects.
</description>
</component>
<!-- ************************************************************************* -->
<component>
......
docs/docs/term_index.xml
View file @ d0f66410
......@@ -411,6 +411,10 @@
<term link="algorithms.html#svm_pegasos" name="svm_pegasos"/>
<term link="algorithms.html#batch" name="batch"/>
<term link="algorithms.html#verbose_batch" name="verbose_batch"/>
<term link="algorithms.html#batch_trainer" name="batch_trainer"/>
<term link="algorithms.html#randomize_samples" name="randomize_samples"/>
<term link="algorithms.html#is_binary_classification_problem" name="is_binary_classification_problem"/>
<term link="algorithms.html#square_root" name="square_root"/>
......@@ -447,6 +451,7 @@
<term link="algorithms.html#distance_function" name="distance_function"/>
<term link="algorithms.html#probabilistic_decision_function" name="probabilistic_decision_function"/>
<term link="algorithms.html#linear_kernel" name="linear_kernel"/>
<term link="algorithms.html#offset_kernel" name="offset_kernel"/>
<term link="algorithms.html#polynomial_kernel" name="polynomial_kernel"/>
<term link="algorithms.html#sigmoid_kernel" name="sigmoid_kernel"/>
<term link="algorithms.html#radial_basis_kernel" name="radial_basis_kernel"/>
......
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