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Commit 7b00cf29 authored by Davis King's avatar Davis King
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Reorganized the menu on the right side of the page. 

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docs/docs/ml.xml
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...@@ -21,7 +21,8 @@ ...@@ -21,7 +21,8 @@
A good tutorial and introduction to the general concepts used by most of the A good tutorial and introduction to the general concepts used by most of the
objects in this part of the library can be found in the <a href="svm_ex.cpp.html">svm example</a> program. objects in this part of the library can be found in the <a href="svm_ex.cpp.html">svm example</a> program.
After reading this example another good one to consult would be the <a href="model_selection_ex.cpp.html">model selection</a> After reading this example another good one to consult would be the <a href="model_selection_ex.cpp.html">model selection</a>
example program. example program. Finally, if you came here looking for a binary classification or regression tool then I would
try the <a href="#krr_trainer">krr_trainer</a> first as it is generally the easiest method to use.
</p> </p>
<p> <p>
...@@ -61,29 +62,79 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf" ...@@ -61,29 +62,79 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<menu width="150"> <menu width="150">
<top> <top>
<center><h2><u>Primary Algorithms</u></h2></center>
<section> <section>
<name>Primary Algorithms</name> <name>Binary Classification</name>
<item>mlp</item>
<item>krls</item>
<item>kcentroid</item>
<item>linearly_independent_subset_finder</item>
<item>linear_manifold_regularizer</item>
<item>empirical_kernel_map</item>
<item>kkmeans</item>
<item>svm_nu_trainer</item> <item>svm_nu_trainer</item>
<item>svm_one_class_trainer</item>
<item>svm_c_trainer</item> <item>svm_c_trainer</item>
<item>svm_c_linear_trainer</item> <item>svm_c_linear_trainer</item>
<item>svm_c_ekm_trainer</item> <item>svm_c_ekm_trainer</item>
<item>rvm_trainer</item> <item>rvm_trainer</item>
<item>svm_pegasos</item>
<item>train_probabilistic_decision_function</item>
</section>
<section>
<name>Multiclass Classification</name>
<item>one_vs_one_trainer</item>
<item>one_vs_all_trainer</item>
</section>
<section>
<name>Regression</name>
<item>mlp</item>
<item>krls</item>
<item>krr_trainer</item> <item>krr_trainer</item>
<item>svr_trainer</item> <item>svr_trainer</item>
<item>rvm_regression_trainer</item> <item>rvm_regression_trainer</item>
<item>rbf_network_trainer</item> <item>rbf_network_trainer</item>
</section>
<section>
<name>Unsupervised</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>
</section>
<section>
<name>Semi-Unsupervised</name>
<item>linear_manifold_regularizer</item>
<item>discriminant_pca</item>
<item nolink="true">
<name>manifold_regularization_tools</name>
<sub>
<item>sample_pair</item>
<item>find_percent_shortest_edges_randomly</item>
<item>find_k_nearest_neighbors</item>
<item>find_approximate_k_nearest_neighbors</item>
<item>remove_short_edges</item>
<item>remove_long_edges</item>
<item>remove_percent_longest_edges</item>
<item>remove_percent_shortest_edges</item>
<item>squared_euclidean_distance</item>
<item>use_weights_of_one</item>
<item>use_gaussian_weights</item>
</sub>
</item>
</section>
<section>
<name>Feature Selection</name>
<item>rank_features</item> <item>rank_features</item>
<item>svm_pegasos</item> <item>sort_basis_vectors</item>
<item>one_vs_one_trainer</item> </section>
<item>one_vs_all_trainer</item>
<center><h2><u>Other Tools</u></h2></center>
<section>
<name>Validation</name>
<item>cross_validate_trainer</item>
<item>cross_validate_trainer_threaded</item>
<item>cross_validate_multiclass_trainer</item>
<item>cross_validate_regression_trainer</item>
<item>test_binary_decision_function</item>
<item>test_multiclass_decision_function</item>
<item>test_regression_function</item>
</section> </section>
<section> <section>
...@@ -134,26 +185,10 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf" ...@@ -134,26 +185,10 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<item>sparse_to_dense</item> <item>sparse_to_dense</item>
</section> </section>
<section>
<name>Validation</name>
<item>cross_validate_trainer</item>
<item>cross_validate_trainer_threaded</item>
<item>cross_validate_multiclass_trainer</item>
<item>cross_validate_regression_trainer</item>
<item>test_binary_decision_function</item>
<item>test_multiclass_decision_function</item>
<item>test_regression_function</item>
</section>
<section> <section>
<name>Miscellaneous</name> <name>Miscellaneous</name>
<item>simplify_linear_decision_function</item> <item>simplify_linear_decision_function</item>
<item>train_probabilistic_decision_function</item>
<item>vector_normalizer</item>
<item>vector_normalizer_pca</item>
<item>discriminant_pca</item>
<item>fill_lisf</item> <item>fill_lisf</item>
<item>sort_basis_vectors</item>
<item>randomize_samples</item> <item>randomize_samples</item>
<item>is_binary_classification_problem</item> <item>is_binary_classification_problem</item>
<item>is_learning_problem</item> <item>is_learning_problem</item>
...@@ -162,29 +197,12 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf" ...@@ -162,29 +197,12 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<item>find_gamma_with_big_centroid_gap</item> <item>find_gamma_with_big_centroid_gap</item>
<item>compute_mean_squared_distance</item> <item>compute_mean_squared_distance</item>
<item>kernel_matrix</item> <item>kernel_matrix</item>
<item>find_clusters_using_kmeans</item>
<item> <item>
<name>sparse vectors</name> <name>sparse vectors</name>
<link>dlib/svm/sparse_vector_abstract.h.html#sparse_vectors</link> <link>dlib/svm/sparse_vector_abstract.h.html#sparse_vectors</link>
</item> </item>
<item nolink="true">
<name>manifold_regularization_tools</name>
<sub>
<item>sample_pair</item>
<item>find_percent_shortest_edges_randomly</item>
<item>find_k_nearest_neighbors</item>
<item>find_approximate_k_nearest_neighbors</item>
<item>remove_short_edges</item>
<item>remove_long_edges</item>
<item>remove_percent_longest_edges</item>
<item>remove_percent_shortest_edges</item>
<item>squared_euclidean_distance</item>
<item>use_weights_of_one</item>
<item>use_gaussian_weights</item>
</sub>
</item>
</section> </section>
...@@ -1158,7 +1176,8 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf" ...@@ -1158,7 +1176,8 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<spec_file link="true">dlib/svm/function_abstract.h</spec_file> <spec_file link="true">dlib/svm/function_abstract.h</spec_file>
<description> <description>
This object represents a classification or regression function that was This object represents a classification or regression function that was
learned by a kernel based learning algorithm. learned by a kernel based learning algorithm. Therefore, it is a function
object that takes a sample object and returns a scalar value.
</description> </description>
<examples> <examples>
<example>svm_ex.cpp.html</example> <example>svm_ex.cpp.html</example>
......
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