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</o:shapelayout></xml><![endif]--></head><body lang=PT link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span lang=EN-US>Please consider to contribute to the<o:p></o:p></span></p><p class=MsoNormal><b><span lang=EN-US><o:p> </o:p></span></b></p><p class=MsoNormal><b><span lang=EN-US style='font-size:12.0pt'>Special Session on Transfer Learning<o:p></o:p></span></b></p><p class=MsoNormal><span lang=EN-US style='font-size:12.0pt'>International Work Conference on Artificial Neural Networks, 10-12 June, 2015 - <a href="http://iwann.ugr.es/2015">http://iwann.ugr.es/2015</a><o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Transfer Learning (TL) aims to transfer knowledge acquired in one problem, the source problem, onto another problem, the target problem, dispensing with the bottom-up construction of the target model. The TL approach has gained significant interest in the Machine Learning (ML) community since it paves the way to devise intelligent learning models that can easily be tailored to many different domains of applicability.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>The following aspects have recently contributed to the emergence of TL:<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>• Generalization Theory: TL often produces algorithms with good generalization capability for different problems;<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>• Efficient TL algorithms: TL provides learning models that can be applied with far less computational effort than standard ML methods;<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>• Unlabeled data: TL can be advantageous since unlabeled data can have severe implications in some fields of research, such as in the biomedical field.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Some examples of topics for this special session:<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>• Big Data with Deep Neural Networks;<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>• Generalization Bounds;<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>• Domain Adaptation or Covariate Shift;<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>• Algorithms for TL;<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>• New advancements in TL;<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>• Real-world applications.<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Deadline: 6 February 2015<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Organizers<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Luís M. Silva, Dep. of Mathematics, University of Aveiro, Portugal - lmas@ua.pt<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>Jorge M. Santos, Dep. of Mathematics, School of Engineering, Polytechnic of Porto, Portugal - jms@isep.ipp.pt<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>