<div dir="ltr"><div class="gmail_default" style="font-family:arial,helvetica,sans-serif;font-size:small"><br clear="all"></div><span class="">CALL</span> FOR <span class="">PAPERS</span><br><br><span class="">SIMBAD</span> <span class="">2015</span><br><br>3rd International Workshop on Similarity-Based Pattern Analysis and Recognition<br><br>October 12-14, <span class="">2015</span><br>Copenhagen, Denmark<br><br><a href="http://www.dsi.unive.it/%7Esimbad/2015/" target="_blank">http://www.dsi.unive.it/~<span class="">simbad</span>/<span class="">2015</span>/</a><br><br><br><br>MOTIVATIONS AND OBJECTIVES<br><br>Traditional pattern recognition and
machine learning techniques are intimately linked to the notion of
"feature space." Adopting this view, each object is described in terms
of a vector of numerical attributes and is therefore mapped to a point
in a Euclidean vector space so that the distances between the points
reflect the observed (dis)similarities between the respective objects.
This kind of representation is attractive because such spaces offer
powerful analytical as well as computational tools that are simply not
available in other representations. This approach, however, suffers
from a major intrinsic limitation, which concerns the representational
power of vectorial, feature-based descriptions. In fact, there are
numerous application domains where either it is not possible to find
satisfactory features or they are inefficient for learning purposes.<br><br>In
the last few years, interest around purely (dis)similarity-based
techniques has grown considerably. For example, within the supervised
learning paradigm the well-established kernel-based methods shift the
focus from the choice of an appropriate set of features to the choice of
a suitable kernel, which is related to object similarities. This shift
in focus, however, is only partial, as the classical interpretation of
the notion of a kernel is that it provides an implicit transformation of
the feature space rather than a purely similarity-based
representation. Similarly, in the unsupervised domain, there has been
an increasing interest around pairwise or even multiway algorithms, such
as spectral and graph-theoretic clustering methods, which avoid the use
of features altogether.<br><br>By departing from vector-space
representations one is confronted with the challenging problem of
dealing with (dis)similarities that do not necessarily possess the
Euclidean behavior or do not even obey the requirements of a metric.
The lack of such properties undermines the very foundations of
traditional pattern recognition and machine learning theories and
algorithms and poses totally new theoretical and computational questions
and challenges.<br><br>The aim of this workshop, following those held
in Venice and York, is to consolidate research efforts in this area and
to provide an informal discussion forum for researchers and
practitioners interested in this important yet diverse subject. We aim
at covering a wide range of problems and perspectives, from supervised
to unsupervised learning, from generative to discriminative models, and
from theoretical issues to real-world applications.<br><br>Original,
unpublished <span class="">papers</span> dealing with these issues are solicited. Topics of
interest include (but are of course not limited to):<br><br>- Embedding and embeddability<br>- Graph spectra and spectral geometry<br>- Indefinite and structural kernels<br>- Game-theoretic models of pattern recognition<br>- Characterization of nonmetric behavior<br>- Foundational issues<br>- Measures of metric violations<br>- Learning and combining (dis)similarities<br>- Multiple-instance learning and other set-based approaches<br>- Applications<br><br><br><span class="">PAPER</span> and ABSTRACT SUBMISSION<br><br>We allow three types of contributions.<br><br>Regular <span class="">papers</span> (not exceeding 16 pages LNCS format) must be submitted electronically. The submission site can be found through <a href="http://www.dsi.unive.it/%7Esimbad/2015/index.php/pages/submission" target="_blank">http://www.dsi.unive.it/~<span class="">simbad</span>/<span class="">2015</span>/index.php/pages/submission</a>.
All submissions will be subject to a rigorous peer-review process.
Accepted <span class="">papers</span> will be published in Springer's Lecture Notes in
Computer Science (LNCS) series.<br><br>In addition to regular, original
contributions, we also solicit presentation of <span class="">papers</span> (in any LaTeX
format, no page restriction) that have been recently published
elsewhere. These <span class="">papers</span> will undergo the same review process as regular
ones. If accepted, they will be presented at the workshop, but only an
abstract will be published. Submission of such contribution requires the
additional submission of a two-page abstract in LNCS format with the
original <span class="">paper</span> submitted as supplementary material.<br><br>Finally, we
encourage the submission of two-page abstracts in general. These could
contain preliminary results, topics for discussion, appeals for novel
research directions, or anything else that suits the aim of <span class="">SIMBAD</span> and
underlines the workshop character. Upon acceptance, these submissions
will be assigned a poster presentation and the abstract will be
published.<br><br>Submission implies the willingness of at least one of the authors to register and present the <span class="">paper</span> on acceptance.<br><br><br>INVITED SPEAKERS<br><br>TBA<br><br><br>IMPORTANT DATES<br><br>Submission of <span class="">paper</span> abstract: March 15, <span class="">2015</span><br>Submission of the final version of the <span class="">paper</span>: March 30, <span class="">2015</span><br>Submission of extended abstract only: April 15<br>Notifications: May 30, <span class="">2015</span><br>Camera-ready due: June 30, <span class="">2015</span><br>Conference: October 12-14, <span class="">2015</span><br><br><br>ORGANIZATION<br><br>Program Chairs<br> Aasa Feragen, University of Copenhagen, Denmark<br> Marco Loog, Delft University of Technology, The Netherlands<br> Marcello Pelillo, University of Venice, Italy<br><br>Steering Committee<br>
Joachim Buhmann, ETH Zurich, Switzerland<br>
Robert Duin, Delft University of Technology, The Netherlands<br>
Mario Figueiredo, Technical University of Lisbon, Portugal<br>
Edwin Hancock, University of <span>York</span>, UK<br>
Vittorio Murino, Italian Institute of Technology, Italy<br>
Marcello Pelillo (chair), University of Venice, Italy<br><br>Program Committee [provisional]<br> Ethem Alpaydin, Bogazici University, Turkey<br> ChloÈ-Agathe Azencott, Mines Paris Tech, France<br> Manuele Bicego, University of Verona, Italy<br> Joachim Buhmann, ETH Zurich, Switzerland<br> Tiberio Caetano, NICTA, Australia<br> Umberto Castellani, University of Verona, Italy<br> Veronika Cheplygina, Erasmus Medical Center, The Netherlands<br> Aykut Erdem, Hacettepe University, Turkey<br> Francisco Escolano, University of Alicante, Spain<br> Mario Figueiredo, Technical University of Lisbon, Portugal<br> Ana Fred, Technical University of Lisbon, Portugal<br> Edwin Hancock, University of York, UK<br> Soren Hauberg, Technical University of Denmark, Denmark<br> Christian Igel, University of Copenhagen, Denmark<br> Brijnesh Jain, Technical University of Berlin, Germany<br> Robert Krauthgamer, The Weizmann Institute of Science, Israel<br> Walter Kropatsch, Vienna University of Technology, Austria<br> Xuelong Li, Chinese Academy of Sciences, China<br> Yingyu Liang, Princeton University, USA<br> Vittorio Murino, Italian Institute of Technology, Italy<br> Antonio Robles-Kelly, NICTA, Australia<br> Fabio Roli, University of Cagliari, Italy<br> Luca Rossi, University of Birmingham, UK<br> Samuel Rota Bulo', Bruno Kessler Foundation, Italy<br> Volker Roth, University of Basel, Switzerland<br> Anastasios Sidiropoulos, Ohio State University, USA<br> Stefan Sommer, University of Copenhagen, Denmark<br> David Tax, Delft University of Technology, The Netherlands<br> Andrea Torsello, University of Venice, Italy<br> Richard Wilson, University of York, UK
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