[libfann] 116/242: Fixed alphabetical order in bibliography, added more information regarding weight initialization, 'see also' info for fann_*_weights
Christian Kastner
chrisk-guest at moszumanska.debian.org
Sat Oct 4 21:10:26 UTC 2014
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chrisk-guest pushed a commit to tag Version2_0_0
in repository libfann.
commit bc42a7278f40229ce8e96f0b37c7afbc91909c84
Author: Evan Nemerson <evan at coeus-group.com>
Date: Wed Mar 31 02:28:53 2004 +0000
Fixed alphabetical order in bibliography, added more information regarding weight initialization, 'see also' info for fann_*_weights
---
doc/fann.xml | 96 +++++++++++++++++++++++++++++++++++++++++++++++-------------
1 file changed, 76 insertions(+), 20 deletions(-)
diff --git a/doc/fann.xml b/doc/fann.xml
index 64dd78c..5cdff64 100644
--- a/doc/fann.xml
+++ b/doc/fann.xml
@@ -71,7 +71,22 @@
</section>
<section id="intro.install.win32">
<title>Windows</title>
- <para>Instructions for Borland & VC++</para>
+ <para>
+ FANN >= 1.1.0 includes a Microsoft Visual C++ 6.0 project file, which can be used to compile FANN for Windows.
+ To build the library and examples with MSVC++ 6.0:
+ </para>
+ <!-- Thanks to Koen Tanghe for this part. -->
+ <para>
+ First, navigate to the MSVC++ directory in the FANN distribution and open the <filename>all.dsw</filename> workspace.
+ In the Visual Studio menu bar, choose "Build" -> "Batch build...", select the project configurations
+ that you would like to build (by default, all are selected), and press "rebuild all"
+ </para>
+ <para>
+ When the build process is complete, the library and examples can be found in the <filename class="directory">MSVC++\Debug</filename> and
+ <filename class="directory">MSVC++\Release</filename> directories and the release versions of the examples are automatically copied inot
+ the <filename class="directory">examples</filename> where they are supposed to be run.
+ </para>
+ <!-- /Koen -->
</section>
<section id="intro.install.src">
<title id="intro.install.src.title">Compiling from source</title>
@@ -233,6 +248,11 @@ int main()
<link linkend="api.fann_init_weights"><function>fann_init_weights</function></link> function.
</para>
<para>
+ In [<xref linkend="bib.fiesler_1997" endterm="bib.fiesler_1997.abbrev"/>], Thimm and Fiesler state that, "An <emphasis>(sic)</emphasis> fixed weight
+ variance of 0.2, which corresponds to a weight range of [-0.77, 0.77], gave the best mean performance for all the applications tested in this study. This
+ performance is similar or better as compared to those of the other weight initialization methods."
+ </para>
+ <para>
The standard activation function is the sigmoid activation function, but it is also possible to use the threshold activation function. A list of the
currently available activation functions is available in the <link linkend="api.sec.constants.activation" endterm="api.sec.constants.activation.title"/>
section. The activation functions are chosen using the
@@ -820,6 +840,10 @@ fann_destroy(ann2);
<para>
Randomizes the weight of each connection in <parameter>ann</parameter>, effectively resetting the network.
</para>
+ <para>
+ See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
+ <link linkend="api.fann_init_weights"><function>fann_init_weights</function></link>
+ </para>
<para>This function appears in FANN >= 1.0.0.</para>
</refsect1>
</refentry>
@@ -847,6 +871,10 @@ fann_destroy(ann2);
The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second
argument, <parameter>data</parameter>, which is the training data that will be used to train the network.
</para>
+ <para>
+ See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
+ <link linkend="api.fann_randomize_weights"><function>fann_randomize_weights</function></link>
+ </para>
<para>This function appears in FANN >= 1.1.0.</para>
</refsect1>
</refentry>
@@ -3315,6 +3343,10 @@ else
<function>fann_randomize_weights</function> will randomize the weights of all neurons in
<parameter>ann</parameter>, effectively resetting the network.
</para>
+ <para>
+ See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
+ <link linkend="api.fann_init_weights"><function>fann_init_weights</function></link>
+ </para>
<para>This function appears in FANN-PHP >= 0.1.0.</para>
</refsect1>
</refentry>
@@ -3346,6 +3378,10 @@ else
The algorithm requires access to the range of the input data (ie, largest and smallest input), and therefore accepts a second
argument, <parameter>data</parameter>, which is the training data that will be used to train the network.
</para>
+ <para>
+ See also: <link linkend="adv.adj" endterm="adv.adj.title" />,
+ <link linkend="api.fann_randomize_weights"><function>fann_randomize_weights</function></link>
+ </para>
<para>This function appears in FANN-PHP >= 0.1.0.</para>
</refsect1>
</refentry>
@@ -3914,6 +3950,25 @@ else
<pubdate>1990</pubdate>
<title id="bib.lecun_1990.title">Advances in Neural Information Processing Systems II</title>
</biblioentry>
+ <biblioentry id="bib.nguyen_1990">
+ <abbrev id="bib.nguyen_1990.abbrev">Nguyen and Widrow, 1990</abbrev>
+ <title id="bib.nguyen_1990.title">Reinforcement Learning</title>
+ <author>
+ <firstname>Derrick</firstname>
+ <surname>Nguyen</surname>
+ </author>
+ <author>
+ <firstname>Bernard</firstname>
+ <surname>Widrow</surname>
+ </author>
+ <pubdate>1990</pubdate>
+ <publishername>Proc. IJCNN</publishername>
+ <volumenum>3</volumenum>
+ <pagenums>21-26</pagenums>
+ <releaseinfo>
+ <ulink url="http://www.cs.montana.edu/~clemens/nguyen-widrow.pdf">http://www.cs.montana.edu/~clemens/nguyen-widrow.pdf</ulink>
+ </releaseinfo>
+ </biblioentry>
<biblioentry id="bib.nissen_2003">
<abbrev id="bib.nissen_2003.abbrev">Nissen et al., 2003</abbrev>
<author>
@@ -3964,25 +4019,6 @@ else
http://www.hamster.dk/~purple/robot/iBOT/report.pdf</ulink>
</releaseinfo>
</biblioentry>
- <biblioentry id="bib.nguyen_1990">
- <abbrev id="bib.nguyen_1990.abbrev">Nguyen and Widrow, 1990</abbrev>
- <title id="bib.nguyen_1990.title">Reinforcement Learning</title>
- <author>
- <firstname>Derrick</firstname>
- <surname>Nguyen</surname>
- </author>
- <author>
- <firstname>Bernard</firstname>
- <surname>Widrow</surname>
- </author>
- <pubdate>1990</pubdate>
- <publishername>Proc. IJCNN</publishername>
- <volumenum>3</volumenum>
- <pagenums>21-26</pagenums>
- <releaseinfo>
- <ulink url="http://www.cs.montana.edu/~clemens/nguyen-widrow.pdf">http://www.cs.montana.edu/~clemens/nguyen-widrow.pdf</ulink>
- </releaseinfo>
- </biblioentry>
<biblioentry id="bib.OSDN_2003">
<abbrev id="bib.OSDN_2003.abbrev">OSDN, 2003</abbrev>
<pubdate>2003</pubdate>
@@ -4069,6 +4105,26 @@ else
<title id="bib.tettamanzi_2001.title">Soft Computing</title>
<publishername>Springer-Verlag</publishername>
</biblioentry>
+ <biblioentry id="bib.fiesler_1997">
+ <abbrev id="bib.fiesler_1997.abbrev">Thimm and Fiesler, High-Order and Multilayer Perceptron Initialization, 1997</abbrev>
+ <author>
+ <firstname>Georg</firstname>
+ <surname>Thimm</surname>
+ </author>
+ <author>
+ <firstname>Emile</firstname>
+ <surname>Fiesler</surname>
+ </author>
+ <pubdate>March 1997</pubdate>
+ <title id="bib.fiesler_1997.title">High-Order and Multilayer Perceptron Initialization</title>
+ <publishername>IEEE Transactions on Neural Networks</publishername>
+ <volumenum>8</volumenum>
+ <issuenum>2</issuenum>
+ <pagenums>249-259</pagenums>
+ <releaseinfo>
+ <ulink url="http://citeseer.ist.psu.edu/thimm96high.html">http://citeseer.ist.psu.edu/thimm96high.html</ulink>
+ </releaseinfo>
+ </biblioentry>
<biblioentry id="bib.thimm_1997">
<abbrev id="bib.thimm_1997.abbrev">Thimm and Fiesler, 1997</abbrev>
<author>
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