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updated docs
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Davis King
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<li>Structural SVM tools for solving <a href="ml.html#structural_assignment_trainer">assignment problems</a> </li>
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<li>Structural SVM tools for <a href="ml.html#structural_object_detection_trainer">object detection</a> in images </li>
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<li>Structural SVM tools for <a href="ml.html#structural_graph_labeling_trainer">labeling nodes</a> in graphs </li>
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<li>A large-scale <a href="ml.html#svm_rank_trainer">SVM-Rank</a> implementation</li>
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<li>An online <a href="ml.html#krls">kernel RLS regression</a> algorithm</li>
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<li>An online <a href="ml.html#svm_pegasos">SVM classification</a> algorithm</li>
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<li>An online kernelized <a href="ml.html#kcentroid">centroid estimator</a>/novelty detector and
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@ -13,7 +13,7 @@
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This page documents all the machine learning algorithms present in
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the library. In particular, there are algorithms for performing
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classification, regression, clustering, sequence labeling, assignment learning,
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graph segmentation, object detection, anomaly detection,
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rank learning, graph segmentation, object detection, anomaly detection,
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and feature ranking, as well as algorithms for doing more
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specialized computations.
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</p>
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@ -116,6 +116,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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<item>structural_sequence_labeling_trainer</item>
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<item>structural_assignment_trainer</item>
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<item>structural_graph_labeling_trainer</item>
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<item>svm_rank_trainer</item>
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</section>
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<section>
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<name>Clustering</name>
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@ -158,6 +159,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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<item>cross_validate_sequence_labeler</item>
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<item>cross_validate_assignment_trainer</item>
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<item>cross_validate_graph_labeling_trainer</item>
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<item>cross_validate_ranking_trainer</item>
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<item>test_binary_decision_function</item>
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<item>test_multiclass_decision_function</item>
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<item>test_regression_function</item>
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@ -165,6 +167,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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<item>test_sequence_labeler</item>
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<item>test_assignment_function</item>
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<item>test_graph_labeling_function</item>
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<item>test_ranking_function</item>
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</section>
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<section>
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@ -242,6 +245,9 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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<item>compute_mean_squared_distance</item>
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<item>kernel_matrix</item>
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<item>sparse vectors</item>
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<item>ranking_pair</item>
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<item>is_ranking_problem</item>
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<item>count_ranking_inversions</item>
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@ -335,6 +341,20 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>ranking_pair</name>
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<file>dlib/svm.h</file>
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<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
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<description>
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This object is used to contain a ranking example. Therefore, ranking_pair
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objects are used to represent training examples for learning-to-rank tasks,
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such as those used by the <a href="#svm_rank_trainer">svm_rank_trainer</a>.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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@ -350,6 +370,34 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>is_ranking_problem</name>
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<file>dlib/svm.h</file>
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<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
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<description>
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This function takes a set of training data for a learning-to-rank problem
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and reports back if it could possibly be a well formed problem.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>count_ranking_inversions</name>
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<file>dlib/svm.h</file>
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<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
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<description>
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Given two sets of objects, X and Y, and an ordering relationship defined
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between their elements, this function counts how many times we see an element
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in the set Y ordered before an element in the set X. Additionally, this
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routine executes efficiently in O(n*log(n)) time via the use of quick sort.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component checked="true">
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@ -1092,6 +1140,28 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>svm_rank_trainer</name>
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<file>dlib/svm.h</file>
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<spec_file link="true">dlib/svm/svm_rank_trainer_abstract.h</spec_file>
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<description>
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This object represents a tool for training a ranking support vector machine
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using linear kernels. In particular, this object is a tool for training
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the Ranking SVM described in the paper:
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<blockquote>
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Optimizing Search Engines using Clickthrough Data by Thorsten Joachims
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</blockquote>
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Finally, note that the implementation of this object is done using the
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<a href="optimization.html#oca">oca</a> optimizer and
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<a href="#count_ranking_inversions">count_ranking_inversions</a> method.
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This means that it runs in O(n*log(n)) time, making it suitable for use
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with large datasets.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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@ -2229,6 +2299,20 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>cross_validate_ranking_trainer</name>
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<file>dlib/svm.h</file>
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<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
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<description>
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Performs k-fold cross validation on a user supplied ranking trainer object such
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as the <a href="#svm_rank_trainer">svm_rank_trainer</a>
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and returns the fraction of ranking pairs ordered correctly.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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@ -2274,6 +2358,19 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
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</component>
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<!-- ************************************************************************* -->
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<component>
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<name>test_ranking_function</name>
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<file>dlib/svm.h</file>
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<spec_file link="true">dlib/svm/ranking_tools_abstract.h</spec_file>
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<description>
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Tests a <a href="#decision_function">decision_function</a>'s ability to correctly
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rank a dataset and returns the resulting ranking accuracy.
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</description>
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</component>
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<!-- ************************************************************************* -->
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<component>
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@ -250,6 +250,7 @@
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<term name="max_index_plus_one">
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<term link="graph_tools.html#max_index_plus_one" name="for graphs"/>
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<term link="dlib/svm/sparse_vector_abstract.h.html#max_index_plus_one" name="for sparse vectors"/>
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<term link="dlib/svm/ranking_tools_abstract.h.html#max_index_plus_one" name="for ranking_pairs"/>
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</term>
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<term file="dlib/svm/sparse_vector_abstract.h.html" name="sparse_matrix_vector_multiply"/>
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<term file="dlib/svm/sparse_vector_abstract.h.html" name="add_to"/>
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<term file="ml.html" name="svm_c_trainer"/>
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<term file="ml.html" name="svm_one_class_trainer"/>
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<term file="ml.html" name="svm_c_linear_trainer"/>
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<term file="ml.html" name="svm_rank_trainer"/>
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<term file="ml.html" name="ranking_pair"/>
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<term file="ml.html" name="is_ranking_problem"/>
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<term file="ml.html" name="count_ranking_inversions"/>
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<term file="ml.html" name="test_ranking_function"/>
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<term file="ml.html" name="cross_validate_ranking_trainer"/>
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<term file="ml.html" name="svm_c_ekm_trainer"/>
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<term file="ml.html" name="rvm_trainer"/>
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<term file="ml.html" name="krr_trainer"/>
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