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docs/docs/index.xml
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<li>Structural SVM tools for solving <a href="ml.html#structural_assignment_trainer">assignment problems</a> </li>
<li>Structural SVM tools for <a href="ml.html#structural_object_detection_trainer">object detection</a> in images </li>
<li>Structural SVM tools for <a href="ml.html#structural_graph_labeling_trainer">labeling nodes</a> in graphs </li>
<li>A large-scale <a href="ml.html#svm_rank_trainer">SVM-Rank</a> implementation</li>
<li>An online <a href="ml.html#krls">kernel RLS regression</a> algorithm</li>
<li>An online <a href="ml.html#svm_pegasos">SVM classification</a> algorithm</li>
<li>An online kernelized <a href="ml.html#kcentroid">centroid estimator</a>/novelty detector and
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docs/docs/ml.xml
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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,
graph segmentation, object detection, anomaly detection,
rank learning, graph segmentation, object detection, anomaly detection,
and feature ranking, as well as algorithms for doing more
specialized computations.
</p>
......@@ -116,6 +116,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<item>structural_sequence_labeling_trainer</item>
<item>structural_assignment_trainer</item>
<item>structural_graph_labeling_trainer</item>
<item>svm_rank_trainer</item>
</section>
<section>
<name>Clustering</name>
......@@ -158,6 +159,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<item>cross_validate_sequence_labeler</item>
<item>cross_validate_assignment_trainer</item>
<item>cross_validate_graph_labeling_trainer</item>
<item>cross_validate_ranking_trainer</item>
<item>test_binary_decision_function</item>
<item>test_multiclass_decision_function</item>
<item>test_regression_function</item>
......@@ -165,6 +167,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<item>test_sequence_labeler</item>
<item>test_assignment_function</item>
<item>test_graph_labeling_function</item>
<item>test_ranking_function</item>
</section>
<section>
......@@ -242,6 +245,9 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
<item>compute_mean_squared_distance</item>
<item>kernel_matrix</item>
<item>sparse vectors</item>
<item>ranking_pair</item>
<item>is_ranking_problem</item>
<item>count_ranking_inversions</item>
......@@ -335,6 +341,20 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
</component>
<!-- ************************************************************************* -->
<component>
<name>ranking_pair</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
This object is used to contain a ranking example. Therefore, ranking_pair
objects are used to represent training examples for learning-to-rank tasks,
such as those used by the <a href="#svm_rank_trainer">svm_rank_trainer</a>.
</description>
</component>
<!-- ************************************************************************* -->
<component>
......@@ -350,6 +370,34 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
</component>
<!-- ************************************************************************* -->
<component>
<name>is_ranking_problem</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
This function takes a set of training data for a learning-to-rank problem
and reports back if it could possibly be a well formed problem.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>count_ranking_inversions</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
Given two sets of objects, X and Y, and an ordering relationship defined
between their elements, this function counts how many times we see an element
in the set Y ordered before an element in the set X. Additionally, this
routine executes efficiently in O(n*log(n)) time via the use of quick sort.
</description>
</component>
<!-- ************************************************************************* -->
<component checked="true">
......@@ -1092,6 +1140,28 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
</component>
<!-- ************************************************************************* -->
<component>
<name>svm_rank_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_rank_trainer_abstract.h</spec_file>
<description>
This object represents a tool for training a ranking support vector machine
using linear kernels. In particular, this object is a tool for training
the Ranking SVM described in the paper:
<blockquote>
Optimizing Search Engines using Clickthrough Data by Thorsten Joachims
</blockquote>
Finally, note that the implementation of this object is done using the
<a href="optimization.html#oca">oca</a> optimizer and
<a href="#count_ranking_inversions">count_ranking_inversions</a> method.
This means that it runs in O(n*log(n)) time, making it suitable for use
with large datasets.
</description>
</component>
<!-- ************************************************************************* -->
<component>
......@@ -2229,6 +2299,20 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
</component>
<!-- ************************************************************************* -->
<component>
<name>cross_validate_ranking_trainer</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
Performs k-fold cross validation on a user supplied ranking trainer object such
as the <a href="#svm_rank_trainer">svm_rank_trainer</a>
and returns the fraction of ranking pairs ordered correctly.
</description>
</component>
<!-- ************************************************************************* -->
<component>
......@@ -2274,6 +2358,19 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
</component>
<!-- ************************************************************************* -->
<component>
<name>test_ranking_function</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
<description>
Tests a <a href="#decision_function">decision_function</a>'s ability to correctly
rank a dataset and returns the resulting ranking accuracy.
</description>
</component>
<!-- ************************************************************************* -->
<component>
......
docs/docs/term_index.xml
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<term name="max_index_plus_one">
<term link="graph_tools.html#max_index_plus_one" name="for graphs"/>
<term link="dlib/svm/sparse_vector_abstract.h.html#max_index_plus_one" name="for sparse vectors"/>
<term link="dlib/svm/ranking_tools_abstract.h.html#max_index_plus_one" name="for ranking_pairs"/>
</term>
<term file="dlib/svm/sparse_vector_abstract.h.html" name="sparse_matrix_vector_multiply"/>
<term file="dlib/svm/sparse_vector_abstract.h.html" name="add_to"/>
......@@ -278,6 +279,12 @@
<term file="ml.html" name="svm_c_trainer"/>
<term file="ml.html" name="svm_one_class_trainer"/>
<term file="ml.html" name="svm_c_linear_trainer"/>
<term file="ml.html" name="svm_rank_trainer"/>
<term file="ml.html" name="ranking_pair"/>
<term file="ml.html" name="is_ranking_problem"/>
<term file="ml.html" name="count_ranking_inversions"/>
<term file="ml.html" name="test_ranking_function"/>
<term file="ml.html" name="cross_validate_ranking_trainer"/>
<term file="ml.html" name="svm_c_ekm_trainer"/>
<term file="ml.html" name="rvm_trainer"/>
<term file="ml.html" name="krr_trainer"/>
......
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