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Improved the ML web page a little

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Davis King 2010-12-28 03:46:40 +00:00
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@ -86,19 +86,13 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<section>
<name>Trainer Adapters</name>
<item>train_probabilistic_decision_function</item>
<item>reduced_decision_function_trainer</item>
<item>reduced</item>
<item>reduced_decision_function_trainer2</item>
<item>reduced2</item>
<item>batch</item>
<item>verbose_batch</item>
<item>batch_cached</item>
<item>verbose_batch_cached</item>
<item>batch_trainer</item>
<item>null_trainer_type</item>
<item>null_trainer</item>
<item>roc_trainer_type</item>
<item>roc_c1_trainer</item>
<item>roc_c2_trainer</item>
</section>
@ -137,6 +131,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<section>
<name>Miscellaneous</name>
<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>
@ -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>
<description>
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
of <a href="#krr_trainer">kernel ridge regression</a>. This means
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>
<description>
<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.
</p>
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>
<description>
<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.
</p>
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>
<description>
This object represents a tool for training the C formulation of
a support vector machine and is optimized for the case where
linear kernels are used.
It is implemented using the <a href="optimization.html#oca">oca</a>
a support vector machine to solve binary classification problems.
It is optimized for the case where linear kernels are used and
is implemented using the <a href="optimization.html#oca">oca</a>
optimizer and uses the exact line search described in the
following paper:
<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>
<description>
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
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>
<spec_file link="true">dlib/svm/svm_threaded_abstract.h</spec_file>
<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>.
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.
@ -1707,7 +1705,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/svm_abstract.h</spec_file>
<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>.
</description>
<examples>