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en:code [2012/12/19 18:08] – grandvalen:code [2023/09/01 10:03] (current) – grandval
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 ====== Online Code ====== ====== Online Code ======
  
-These softwares are provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall we be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.+These pieces of code are provided for facilitating dissemination and reproduction of published experimental results. Any bug report is welcomed, but please refrain to ask for some installation support from the authors.
  
-Any bug report is welcomed. These codes are provided for facilitating dissemination and reproduction of published experimental results. Please refrain to ask for some installation support from the authors.+As usual, these codes are provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and noninfringement. In no event shall we be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the software or the use or other dealings in the software.
  
 +Adaptive Ridge Regression a.k.a lasso
 +[[https://www.hds.utc.fr/~grandval/arrfit.m|Matlab function[.m]]], introduced in [[https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=c01ea6fd3c31151dd95f94165256d07777e62789|Least absolute shrinkage is equivalent to quadratic penalization]] and generalized to group-lasso in 
 + [[https://proceedings.neurips.cc/paper_files/paper/1998/file/cfa5301358b9fcbe7aa45b1ceea088c6-Paper.pdf|Outcomes of the equivalence of adaptive ridge with least absolute shrinkage]].
  
-<html> +Sparse Linear Discriminant Analysis by GLOSS [[https://www.hds.utc.fr/~grandval/OSpenfitvar1.m|Matlab function[.m]]], introduced in [[https://icml.cc/Conferences/2012/papers/591.pdf|An Efficient Approach to Sparse Linear Discriminant Analysis]].
-<table> +
-<tr> +
-<td width=60% align=left valign=top> Adaptive Ridge Regression a.k.a lasso</td> +
-<td width=5% align=right valign=top><a HREF="../arrfit.m">Code[.m]</a></td> +
-<td width=35% align=right valign=top> +
-  <a HREF="../icann98.pdf">ICANN paper[.pdf]</a>    +
-  <a HREF="../nips98.pdf">NIPS paper[.pdf]</a></td> +
-</tr> +
-<tr> +
-<td width=60% align=left valign=top> Sparse Linear Discriminant Analysis by GLOSS</td> +
-<td width=5% align=right valign=top><a HREF="../OSpenfitvar1.m">Code[.m]</a></td> +
-<td width=35% align=right valign=top> +
-  <a HREF="http://arxiv.org/abs/1206.6472">ICML paper at arXiv</a></td> +
-</tr> +
-</table> +
-</html>+
  

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en/code.1355936890.txt.gz · Last modified: 2012/12/19 18:08 by grandval
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