OrgLion-ML
/
dlib
mirror of https://github.com/davisking/dlib.git
1
0
Fork 0
Code Issues Releases Wiki Activity

Improved the ML web page a little 

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404045
This commit is contained in:
Davis King 2010-12-28 03:46:40 +00:00
parent 472279c3db
commit f9aa70c723
1 changed files with 13 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

28
docs/docs/ml.xml
View File

@ -86,19 +86,13 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<section> <section>
<name>Trainer Adapters</name> <name>Trainer Adapters</name>
<item>train_probabilistic_decision_function</item>
<item>reduced_decision_function_trainer</item>
<item>reduced</item> <item>reduced</item>
<item>reduced_decision_function_trainer2</item>
<item>reduced2</item> <item>reduced2</item>
<item>batch</item> <item>batch</item>
<item>verbose_batch</item> <item>verbose_batch</item>
<item>batch_cached</item> <item>batch_cached</item>
<item>verbose_batch_cached</item> <item>verbose_batch_cached</item>
<item>batch_trainer</item>
<item>null_trainer_type</item>
<item>null_trainer</item> <item>null_trainer</item>
<item>roc_trainer_type</item>
<item>roc_c1_trainer</item> <item>roc_c1_trainer</item>
<item>roc_c2_trainer</item> <item>roc_c2_trainer</item>
</section> </section>
@ -137,6 +131,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<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</item>
<item>vector_normalizer_pca</item> <item>vector_normalizer_pca</item>
<item>discriminant_pca</item> <item>discriminant_pca</item>
@ -837,6 +832,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<spec_file link="true">dlib/svm/rbf_network_abstract.h</spec_file> <spec_file link="true">dlib/svm/rbf_network_abstract.h</spec_file>
<description> <description>
Trains a radial basis function network and outputs a <a href="#decision_function">decision_function</a>. Trains a radial basis function network and outputs a <a href="#decision_function">decision_function</a>.
This object can be used for either regression or binary classification problems.
It's worth pointing out that this object is essentially an unregularized version It's worth pointing out that this object is essentially an unregularized version
of <a href="#krr_trainer">kernel ridge regression</a>. This means of <a href="#krr_trainer">kernel ridge regression</a>. This means
you should really prefer to use kernel ridge regression instead. you should really prefer to use kernel ridge regression instead.
@ -950,7 +946,8 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<spec_file link="true">dlib/svm/svm_nu_trainer_abstract.h</spec_file> <spec_file link="true">dlib/svm/svm_nu_trainer_abstract.h</spec_file>
<description> <description>
<p> <p>
Trains a nu support vector classifier and outputs a <a href="#decision_function">decision_function</a>. Trains a nu support vector machine for solving binary classification problems and
outputs a <a href="#decision_function">decision_function</a>.
It is implemented using the <a href="optimization.html#solve_qp2_using_smo">SMO</a> algorithm. It is implemented using the <a href="optimization.html#solve_qp2_using_smo">SMO</a> algorithm.
</p> </p>
The implementation of the nu-svm training algorithm used by this library is based The implementation of the nu-svm training algorithm used by this library is based
@ -999,7 +996,8 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<spec_file link="true">dlib/svm/svm_c_trainer_abstract.h</spec_file> <spec_file link="true">dlib/svm/svm_c_trainer_abstract.h</spec_file>
<description> <description>
<p> <p>
Trains a C support vector classifier and outputs a <a href="#decision_function">decision_function</a>. Trains a C support vector machine for solving binary classification problems
and outputs a <a href="#decision_function">decision_function</a>.
It is implemented using the <a href="optimization.html#solve_qp3_using_smo">SMO</a> algorithm. It is implemented using the <a href="optimization.html#solve_qp3_using_smo">SMO</a> algorithm.
</p> </p>
The implementation of the C-SVM training algorithm used by this library is based The implementation of the C-SVM training algorithm used by this library is based
@ -1021,10 +1019,9 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<spec_file link="true">dlib/svm/svm_c_linear_trainer_abstract.h</spec_file> <spec_file link="true">dlib/svm/svm_c_linear_trainer_abstract.h</spec_file>
<description> <description>
This object represents a tool for training the C formulation of This object represents a tool for training the C formulation of
a support vector machine and is optimized for the case where a support vector machine to solve binary classification problems.
linear kernels are used. It is optimized for the case where linear kernels are used and
is implemented using the <a href="optimization.html#oca">oca</a>
It is implemented using the <a href="optimization.html#oca">oca</a>
optimizer and uses the exact line search described in the optimizer and uses the exact line search described in the
following paper: following paper:
<blockquote> <blockquote>
@ -1047,7 +1044,8 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<spec_file link="true">dlib/svm/svm_c_ekm_trainer_abstract.h</spec_file> <spec_file link="true">dlib/svm/svm_c_ekm_trainer_abstract.h</spec_file>
<description> <description>
This object represents a tool for training the C formulation of This object represents a tool for training the C formulation of
a support vector machine. It is implemented using the <a href="#empirical_kernel_map">empirical_kernel_map</a> a support vector machine for solving binary classification problems.
It is implemented using the <a href="#empirical_kernel_map">empirical_kernel_map</a>
to kernelize the <a href="#svm_c_linear_trainer">svm_c_linear_trainer</a>. This makes it a very fast algorithm to kernelize the <a href="#svm_c_linear_trainer">svm_c_linear_trainer</a>. This makes it a very fast algorithm
capable of learning from very large datasets. capable of learning from very large datasets.
@ -1691,7 +1689,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<file>dlib/svm_threaded.h</file> <file>dlib/svm_threaded.h</file>
<spec_file link="true">dlib/svm/svm_threaded_abstract.h</spec_file> <spec_file link="true">dlib/svm/svm_threaded_abstract.h</spec_file>
<description> <description>
Performs k-fold cross validation on a user supplied trainer object such Performs k-fold cross validation on a user supplied binary classification trainer object such
as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or <a href="#rbf_network_trainer">rbf_network_trainer</a>. as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or <a href="#rbf_network_trainer">rbf_network_trainer</a>.
This function does the same thing as <a href="#cross_validate_trainer">cross_validate_trainer</a> This function does the same thing as <a href="#cross_validate_trainer">cross_validate_trainer</a>
except this function also allows you to specify how many threads of execution to use. except this function also allows you to specify how many threads of execution to use.
@ -1707,7 +1705,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<file>dlib/svm.h</file> <file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file> <spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<description> <description>
Performs k-fold cross validation on a user supplied trainer object such Performs k-fold cross validation on a user supplied binary classification trainer object such
as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or <a href="#rbf_network_trainer">rbf_network_trainer</a>. as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or <a href="#rbf_network_trainer">rbf_network_trainer</a>.
</description> </description>
<examples> <examples>