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<div class="moz-text-html" lang="x-western"> [Apologies if you
receive multiple copies of this CFP]<br>
<br>
<b>Extended deadline: 15 March 2014 (last extension)</b><br>
<div class="moz-text-html" lang="x-western"> <b><br>
Call for Papers: Neurocomputing Special Issue on "Advances
in Learning with Label Noise</b><b>"</b><br>
<br>
<b><u>AIMS AND SCOPE</u></b><br>
<br>
Label noise is an important issue in classification. It is
both expensive and difficult to obtain completely reliable
labels, yet traditional classifiers expect a perfectly
labelled training set. In real-world data sets, however, the
labels available often contain mistakes. Mislabelling may
occur for several reasons, including lack of information,
speedy labelling by non-experts, the subjective nature of
class memberships, expert errors, and communication problems.
Furthermore, label noise may take several different forms --
for instance, labelling errors may occur at random, or may
depend on particular values of the data features, or they may
be adversarial. Errors may affect all data classes equally or
asymmetrically. A large body of literature exists on the
effects of label noise, which shows that mislabelling may
detrimentally affect the classification performance, the
complexity of the learned models, and it may impair
pre-processing tasks such as feature selection. <br>
<br>
Many methods have been proposed to deal with label noise.
Filter approaches aim at identifying and removing any
mislabelled instances. Label noise sensitive algorithms aim
at dealing with label noise during learning, by modelling the
process of label corruption as part of modelling the data.
Some methods have been modified to take label noise into
account in an embedded fashion. The current literature on
learning with label noise is a lively mixture of theoretical
and experimental studies which clearly demonstrate both the
complexity and the importance of the problem. Dealing with
mislabelled instances needs to be flexible enough to
accommodate label uncertainty, yet constrained enough to guide
the learning process in its decisions regarding when to trust
the label and when to trust the classifier.<br>
<br>
This special issue aims to stimulate new research in the area
of learning with label noise by providing a forum for authors
to report on new advances and findings in this problem area.
Topics of interest include, but are not limited to:<br>
<ul>
<li>new methods to deal with label noise;</li>
<li>new applications where label noise must be taken into
account;</li>
<li>theoretical results about learning in the presence of
label noise;</li>
<li>experimental results which provide insight about
existing methods;</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 meta-parameters/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>on-line learning with label noise</li>
<li>learning with side information to counter label noise;</li>
<li>model assessment in the presence of label noise in test
data.</li>
</ul>
<br>
<u><b>SUBMISSION OF MANUSCRIPTS</b></u><u><b><br>
</b></u><br>
If you intend to contribute to this special issue, please send
a title and abstract of your contribution to the guest
editors.<br>
<br>
Authors should prepare their manuscript according to the Guide
for Authors available at <a
href="http://www.journals.elsevier.com/neurocomputing"
target="_blank">http://www.journals.elsevier.com/neurocomputing</a>.
All the papers will be peer-reviewed following the
Neurocomputing reviewing procedures. Authors must submit
their papers electronically by using online manuscript
submission at <a href="http://ees.elsevier.com/neucom"
target="_blank">http://ees.elsevier.com/neucom</a>. To
ensure that all manuscripts are correctly included into the
special issue, it is important that authors select "SI:
Learning with label noise" when they reach the "Article Type"
step in the submission process.<br>
<br>
For technical questions regarding the submission website,
please contact the support office at Elsevier or the guest
editors.<br>
<br>
<u><b>IMPORTANT DATES</b></u><br>
<br>
Extended deadline of paper submission: 15 March 2014 (last
extension)<br>
Notification of acceptance: 15 July 2014<br>
<br>
<u><b>GUEST EDITORS</b></u><br>
<br>
Benoît Frénay (Managing Guest Editor)<br>
Université catholique de Louvain, Belgium<br>
E-mail: <a href="mailto:benoit.frenay@uclouvain.be"
target="_blank">benoit.frenay@uclouvain.be</a><br>
Website:<a href="http://bfrenay.wordpress.com/">
http://bfrenay.wordpress.com</a><br>
Phone: <a href="tel:%2B32%2010%20478133" value="+3210478133"
target="_blank">+32 10 478133</a><br>
<br>
Ata Kaban (Special Issue Guest Editor)<br>
University of Birmingham, United Kingdom<br>
E-mail: <a href="mailto:A.Kaban@cs.bham.ac.uk"
target="_blank">A.Kaban@cs.bham.ac.uk</a><br>
Website:<a href="http://www.cs.bham.ac.uk/%7Eaxk">
http://www.cs.bham.ac.uk/~axk</a><br>
Phone: <a href="tel:%2B44%20121%2041%2042792"
value="+441214142792" target="_blank">+44 121 41 42792</a><br>
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