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updated docs

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docs/docs/ml.xml
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......@@ -118,13 +118,19 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<item>structural_graph_labeling_trainer</item>
</section>
<section>
<name>Unsupervised</name>
<name>Unsupervised Clustering</name>
<item>kkmeans</item>
<item>find_clusters_using_kmeans</item>
<item>newman_cluster</item>
<item>chinese_whispers</item>
<item>modularity</item>
</section>
<section>
<name>Unsupervised Miscellaneous</name>
<item>kcentroid</item>
<item>linearly_independent_subset_finder</item>
<item>empirical_kernel_map</item>
<item>kkmeans</item>
<item>svm_one_class_trainer</item>
<item>find_clusters_using_kmeans</item>
<item>vector_normalizer</item>
<item>vector_normalizer_pca</item>
<item>sammon_projection</item>
......@@ -149,6 +155,9 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<item>squared_euclidean_distance</item>
<item>use_weights_of_one</item>
<item>use_gaussian_weights</item>
<item>is_ordered_by_index</item>
<item>find_neighbor_ranges</item>
<item>convert_unordered_to_ordered</item>
</sub>
</item>
</section>
......@@ -342,6 +351,46 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
</component>
<!-- ************************************************************************* -->
<component>
<name>is_ordered_by_index</name>
<file>dlib/manifold_regularization.h</file>
<spec_file link="true">dlib/manifold_regularization/graph_creation_abstract.h</spec_file>
<description>
This function checks if a vector of <a href="#sample_pair">sample_pair</a> or
<a href="#ordered_sample_pair">ordered_sample_pair</a> objects is in sorted
order according to their index values.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>convert_unordered_to_ordered</name>
<file>dlib/manifold_regularization.h</file>
<spec_file link="true">dlib/manifold_regularization/graph_creation_abstract.h</spec_file>
<description>
This function takes a graph, defined by a vector of
<a href="#sample_pair">sample_pair</a> objects and converts it into the equivalent
graph defined by a vector of <a href="#ordered_sample_pair">ordered_sample_pair</a> objects.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>find_neighbor_ranges</name>
<file>dlib/manifold_regularization.h</file>
<spec_file link="true">dlib/manifold_regularization/graph_creation_abstract.h</spec_file>
<description>
This function takes a graph, defined by a vector of
<a href="#ordered_sample_pair">ordered_sample_pair</a> objects, and finds the
ranges that contain the edges for each node in the graph. The output therefore
lets you easily locate the neighbors of any node in the graph.
</description>
</component>
<!-- ************************************************************************* -->
<component>
......@@ -457,11 +506,64 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
</component>
<!-- ************************************************************************* -->
<component>
<name>modularity</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/clustering/modularity_clustering_abstract.h</spec_file>
<description>
This function computes the modularity of a particular graph clustering. This
is a number that tells you how good the clustering is. In particular, it
is the measure optimized by the <a href="#newman_cluster">newman_cluster</a>
routine.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>newman_cluster</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/clustering/modularity_clustering_abstract.h</spec_file>
<description>
This function performs the clustering algorithm described in the paper
<blockquote>Modularity and community structure in networks by M. E. J. Newman.</blockquote>
In particular, this is a method for automatically clustering the nodes in a
graph into groups. The method is able to automatically determine the number
of clusters and does not have any parameters. In general, it is a very good
clustering technique.
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>chinese_whispers</name>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/clustering/chinese_whispers_abstract.h</spec_file>
<description>
This function performs the clustering algorithm described in the paper
<blockquote>Chinese Whispers - an Efficient Graph Clustering Algorithm and its
Application to Natural Language Processing Problems by Chris Biemann.</blockquote>
In particular, this is a method for automatically clustering the nodes in a
graph into groups. The method is able to automatically determine the number
of clusters.
<p>
It should be noted that this method is generally not going to work as
well as <a href="#newman_cluster">Newman clustering</a>. However, Chinese
Whispers is very fast.
</p>
</description>
</component>
<!-- ************************************************************************* -->
<component>
<name>find_clusters_using_kmeans</name>
<file>dlib/svm.h</file>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
<description>
This is just a simple linear kmeans clustering implementation.
......@@ -473,7 +575,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<component>
<name>pick_initial_centers</name>
<file>dlib/svm.h</file>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
<description>
This is a function that you can use to seed data clustering algorithms
......@@ -641,7 +743,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<component>
<name>kkmeans</name>
<file>dlib/svm.h</file>
<file>dlib/clustering.h</file>
<spec_file link="true">dlib/svm/kkmeans_abstract.h</spec_file>
<description>
This is an implementation of a kernelized k-means clustering algorithm.
......
docs/docs/term_index.xml
View file @ 1727efea
......@@ -227,6 +227,9 @@
<term file="ml.html" name="find_k_nearest_neighbors"/>
<term file="ml.html" name="remove_short_edges"/>
<term file="ml.html" name="remove_duplicate_edges"/>
<term file="ml.html" name="is_ordered_by_index"/>
<term file="ml.html" name="convert_unordered_to_ordered"/>
<term file="ml.html" name="find_neighbor_ranges"/>
<term file="ml.html" name="remove_long_edges"/>
<term file="ml.html" name="remove_percent_longest_edges"/>
<term file="ml.html" name="remove_percent_shortest_edges"/>
......@@ -236,6 +239,7 @@
<term link="dlib/manifold_regularization/graph_creation_abstract.h.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>
<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"/>
<term file="dlib/svm/sparse_vector_abstract.h.html" name="subtract_from"/>
<term file="dlib/svm/sparse_vector_abstract.h.html" name="assign"/>
......@@ -308,6 +312,9 @@
<term file="dlib/statistics/dpca_abstract.h.html" name="discriminant_pca_error"/>
<term file="ml.html" name="kkmeans"/>
<term file="ml.html" name="find_clusters_using_kmeans"/>
<term file="ml.html" name="newman_cluster"/>
<term file="ml.html" name="chinese_whispers"/>
<term file="ml.html" name="modularity"/>
<term file="ml.html" name="pick_initial_centers"/>
<term file="ml.html" name="rank_features"/>
<term file="ml.html" name="find_gamma_with_big_centroid_gap"/>
......
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