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updated docs

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Davis King 2015-04-29 08:04:04 -04:00
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51
docs/docs/ml.xml
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@ -135,6 +135,10 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<item>vector_normalizer_frobmetric</item>
<item>compute_lda_transform</item>
</section>
<section>
<name>Reinforcement Learning</name>
<item>lspi</item>
</section>
<section>
<name>Feature Selection</name>
<item>rank_features</item>
@ -218,6 +222,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<item>assignment_function</item>
<item>track_association_function</item>
<item>graph_labeler</item>
<item>policy</item>
</section>
<section>
@ -252,6 +257,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<item>is_ranking_problem</item>
<item>count_ranking_inversions</item>
<item>learn_platt_scaling</item>
<item>process_sample</item>
@ -1635,6 +1641,51 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
</examples>
</component>
<!-- ************************************************************************* -->
<component>
<name>lspi</name>
<file>dlib/control.h</file>
<spec_file link="true">dlib/control/lspi_abstract.h</spec_file>
<description>
This object is an implementation of the reinforcement learning algorithm
described in the following paper:
<blockquote>
Lagoudakis, Michail G., and Ronald Parr. "Least-squares policy
iteration." The Journal of Machine Learning Research 4 (2003):
1107-1149.
</blockquote>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>policy</name>
<file>dlib/control.h</file>
<spec_file link="true">dlib/control/approximate_linear_models_abstract.h</spec_file>
<description>
This is a policy (i.e. a control law) based on a linear function approximator.
You can use a tool like <a href="#lspi">lspi</a> to learn the parameters
of a policy.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>process_sample</name>
<file>dlib/control.h</file>
<spec_file link="true">dlib/control/approximate_linear_models_abstract.h</spec_file>
<description>
This object holds a training sample for a reinforcement learning algorithm
(e.g. <a href="#lspi">lspi</a>).
In particular, it contains a state, action, reward, next state sample from
some process.
</description>
</component>
<!-- ************************************************************************* -->
<component>

3
docs/docs/term_index.xml
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@ -267,6 +267,9 @@
<term file="algorithms.html" name="random_subset_selector" include="dlib/statistics.h"/>
<term file="algorithms.html" name="randomly_subsample" include="dlib/statistics.h"/>
<term file="ml.html" name="lspi" include="dlib/control.h"/>
<term file="ml.html" name="policy" include="dlib/control.h"/>
<term file="ml.html" name="process_sample" include="dlib/control.h"/>
<term file="ml.html" name="select_all_distinct_labels" include="dlib/svm.h"/>
<term file="dlib/svm/multiclass_tools_abstract.h.html" name="find_missing_pairs" include="dlib/svm.h"/>