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<div class="moz-text-html" lang="x-western"> [Apologies if you
receive multiple copies of this CFP]<br>
<br>
<b>Call for papers: special session on "</b><b><b>Label noise
in classification</b>" at </b><b>ESANN 2014</b><br>
<br>
European Symposium on Artificial Neural Networks, Computational
Intelligence and<br>
Machine Learning (ESANN 2014). 23-25 April 2014, Bruges, Belgium
- <a class="moz-txt-link-freetext" href="http://www.esann.org">http://www.esann.org</a><br>
<span style="font-family: Verdana; font-size: 13.5pt;
line-height: 20px; "><br>
<br>
</span><b>DESCRIPTION</b>:<br>
<br>
In classification, it is difficult to obtain completely reliable
labels. Indeed, labels are often polluted by label noise, due
to e.g. insufficient information or expert mistakes. Many works
have tackled the problem of learning in the presence of label
noise. Filtering methods have been developed to detect and
remove mislabelled instances. Also, recent approaches attempt
to take label noise into account while learning, using e.g.
probabilistic models of label noise or prior knowledge about the
influence of label noise on specific methods. Other settings
like e.g. semi-supervised learning have also been studied.<br>
<br>
This special session aims to provide a forum where researchers
could discuss the most recent developments in the field of label
noise. Contributions should propose new methods to deal with
label noise, new applications where label noise must be taken
into account, theoretical results about learning in the presence
of label noise or experimental results which provide insight
about existing methods.<br>
<br>
Examples of topics of interest include (but are not limited to)
the following:<br>
<ul>
<li>when are noisy labels better than no labels at all?</li>
<li>what makes a classifier robust to label noise?</li>
<li>dealing with different types of label noise (random,
non-random, malicious, or adversarial)</li>
<li>conditions for the consistency of classification in the
presence of label noise</li>
<li>label noise in high dimensional small sample settings</li>
<li>the issue of model order selection in the presence of
label noise</li>
<li>feature selection and dimensionality reduction in the
presence of label noise</li>
<li>label-noise aware classification algorithms in static and
dynamic scenarios</li>
<li>learning with side information to counter label noise</li>
</ul>
<b><br>
<br>
SUBMISSION:<br>
</b><br>
Prospective authors must submit their paper through the ESANN
portal following the instructions provided in <a
class="moz-txt-link-freetext"
href="http://www.elen.ucl.ac.be/esann/index.php?pg=submission">http://www.elen.ucl.ac.be/esann/index.php?pg=submission</a>.
Each paper will undergo a peer reviewing process for its
acceptance. Authors should send as soon as possible an e-mail
with the tentative title of their contribution to the special
session organisers. Selected papers from ESANN'14 will be
published in a special issue of the Neurocomputing journal.<b>
<br>
<br>
<br>
IMPORTANT DATES</b><b>:</b><br>
<br>
Paper submission deadline : 29 November 2013<br>
Notification of acceptance : 31 January 2014<br>
The ESANN 2014 conference : 23-25 April 2014<br>
<b><br>
<br>
SPECIAL SESSION ORGANISERS</b><b>:</b><br>
<br>
Dr. Benoît Frénay<br>
Université catholique de Louvain, Belgium<br>
E-mail: <a class="moz-txt-link-abbreviated"
href="mailto:benoit.frenay@uclouvain.be">benoit.frenay@uclouvain.be</a><br>
Website:<a moz-do-not-send="true"
href="http://bfrenay.wordpress.com/" target="_blank">
http://bfrenay.wordpress.com</a><br>
Phone: +32 10 478133<br>
<br>
Dr. Ata Kaban<br>
University of Birmingham, United Kingdom<br>
E-mail: <a class="moz-txt-link-abbreviated"
href="mailto:A.Kaban@cs.bham.ac.uk">A.Kaban@cs.bham.ac.uk</a><br>
Website:<a moz-do-not-send="true"
href="http://www.cs.bham.ac.uk/%7Eaxk" target="_blank">
http://www.cs.bham.ac.uk/~axk</a><br>
Phone: +44 121 41 42792 </div>
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