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updated docs

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Davis King 2012-02-22 20:15:09 -05:00
parent 565e63f282
commit f3efbc31b8
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docs/docs/ml.xml
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@ -84,6 +84,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
<name>Regression</name>
<item>mlp</item>
<item>krls</item>
<item>rls</item>
<item>krr_trainer</item>
<item>rr_trainer</item>
<item>svr_trainer</item>
@ -531,6 +532,25 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
</component>
<!-- ************************************************************************* -->
<component>
<name>rls</name>
<file>dlib/svm.h</file>
<spec_file link="true">dlib/svm/rls_abstract.h</spec_file>
<description>
This is an implementation of the linear version of the recursive least
squares algorithm. It accepts training points incrementally and, at
each step, maintains the solution to the following optimization problem:
<blockquote>
find w minimizing: 0.5*dot(w,w) + C*sum_i(y_i - trans(x_i)*w)^2
</blockquote>
Where (x_i,y_i) are training pairs. x_i is some vector and y_i is a target
scalar value.
</description>
</component>
<!-- ************************************************************************* -->
<component>

1
docs/docs/term_index.xml
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@ -258,6 +258,7 @@
<term link="ml.html#krr_trainer" name="regularized least squares"/>
<term link="ml.html#krr_trainer" name="least squares SVM"/>
<term file="ml.html" name="krls"/>
<term file="ml.html" name="rls"/>
<term file="ml.html" name="kcentroid"/>
<term file="ml.html" name="linearly_independent_subset_finder"/>
<term file="ml.html" name="fill_lisf"/>