<div dir="ltr"><div><div><br></div><div> ICCBR-16 Workshop</div><div><br></div><div> Workshop on Synergies between CBR and Knowledge Discovery</div><div><br></div><div><br></div><div> Call for Papers</div><div><br></div><div>At the core of CBR lies the ability of a system to learn from past cases. </div><div><br></div><div>However, CBR systems often incorporate knowledge discovery methods, for </div><div><br></div><div>example, to organize their memory or to learn adaptation rules. In turn, </div><div><br></div><div>knowledge discovery systems often utilize CBR as a learning methodology, for </div><div><br></div><div>example, through a common set of problems with the nearest-neighbor method </div><div><br></div><div>and reinforcement learning. Meanwhile, the machine learning community, </div><div><br></div><div>which is tightly coupled with knowledge discovery, has historically included </div><div><br></div><div>CBR among the types of instance-based learning.</div><div><br></div><div>This workshop will be dedicated to studying in-depth the possible synergies </div><div><br></div><div>between case-based reasoning (CBR) and knowledge discovery. It also aims at </div><div><br></div><div>identifying potentially fruitful ideas for co-operative problem-solving </div><div><br></div><div>where both CBR and knowledge discovery researchers can compare and combine </div><div><br></div><div>methods. In particular, new advances in knowledge discovery may help CBR to </div><div><br></div><div>advance its field of study and play a vital role in the future of knowledge </div><div><br></div><div>discovery. This first Workshop on Synergies between CBR and Knowledge </div><div><br></div><div>discovery aims to:</div><div><br></div><div><br></div><div>* provide a forum for identifying important contributions and </div><div>opportunities for research on combining CBR and knowledge discovery, </div><div>* promote the systematic study of how to synergistically integrate CBR </div><div>and knowledge discovery, </div><div>* showcase synergistic systems using CBR and knowledge discovery. </div><div><br></div><div>Some of the technical issues addressed, and potential outcomes of the </div><div><br></div><div>workshop, are to identify the knowledge discovery methods used in CBR, to </div><div><br></div><div>categorize the problems addressed by knowledge discovery in CBR, to propose </div><div><br></div><div>methodological improvements to fit this context’s needs, preferred types and </div><div><br></div><div>methods, and guidelines to better develop CBR systems taking advantage of </div><div><br></div><div>all knowledge discovery research has to offer. Similarly, the workshop will </div><div><br></div><div>identify the CBR methods used in knowledge discovery, categorize the </div><div><br></div><div>problems addressed by CBR in knowledge discovery, propose methodological </div><div><br></div><div>improvements to fit this context’s needs, preferred types and methods, and </div><div><br></div><div>guidelines to better develop knowledge discovery systems taking advantage of </div><div><br></div><div>all CBR research has to offer.</div><div><br></div><div>We welcome all those interested in the problems and promise of </div><div><br></div><div>synergistically combining CBR and knowledge discovery whether they belong to </div><div><br></div><div>the CBR, the knowledge discovery community, or the machine learning </div><div><br></div><div>community. </div><div><br></div><div><br></div><div>Topics of interest include (but are not limited to):</div><div><br></div><div>* Architectures for synergistic systems between CBR and knowledge discovery</div><div>* Theoretical frameworks for synergistic systems between CBR and data </div><div>mining</div><div>* Memory structure mining in CBR</div><div>* Memory organization mining in CBR (decision tree induction, etc.)</div><div>* Case mining</div><div>* Feature selection in CBR</div><div>* Knowledge discovery in CBR (adaptation knowledge, meta-knowledge, etc.)</div><div>* Concept mining in CBR</div><div>* Image and multimedia mining in CBR</div><div>* Temporal mining in CBR</div><div>* Text mining in CBR</div><div>* Nearest-neighbor systems and CBR</div><div>* Instance-based learning and CBR</div><div>* Reinforcement learning and CBR</div><div>* CBR and statistics</div><div>* CBR and statistical data analysis</div><div>* CBR in multi-strategy learning systems</div><div>* CBR and similarity and metric learning</div><div>* CBR and Big Data</div><div>* CBR and deep learning</div><div>* Application specific synergies between CBR and knowledge discovery </div><div><br></div><div>(medicine, </div><div>bioinformatics, social networks, sentiment analysis, etc.)</div><div><br></div><div>Paper presentations will be interspersed with discussions in which we </div><div>characterize, categorize, and discuss the synergies between CBR and data </div><div>mining. A wrap-up round table discussion will summarize the lessons </div><div>learnt, issues identified, and future directions. </div><div><br></div><div>Submission Requirements</div><div><br></div><div>Submitted papers are limited to 10 pages in length. </div><div><br></div><div>All papers are to be submitted via the CBR-KD-16 EasyChair system </div><div>(<a href="https://www.easychair.org/conferences/?conf=cbrkd2016">https://www.easychair.org/conferences/?conf=cbrkd2016</a>). </div><div>Papers should be in Springer LNCS format. Author's instructions, along </div><div>with LaTeX and Word macro files, are available at </div><div><a href="http://www.springer.de/comp/lncs/authors.html">http://www.springer.de/comp/lncs/authors.html</a>. </div><div><br></div><div>Submissions should be original papers that have not already been published </div><div><br></div><div>elsewhere. However, papers may include previously published results that </div><div><br></div><div>support a new theme, as long as all past publications are fully referenced.</div><div><br></div><div>Dates</div><div>* Submission Deadline: August 12, 2016 (extended deadline)</div><div>* Notification Date: September 5, 2016</div><div>* Camera-Ready Deadline: September 25, 2016</div><div>* Workshop Date: October 31, 2016 (Atlanta, USA)</div><div><br></div><div><br></div><div>Workshop Web Site: <a href="http://cs.oswego.edu/~bichinda/iccbr2016/">http://cs.oswego.edu/~bichinda/iccbr2016/</a></div><div><br></div><div>Submission Site: <a href="http://www.easychair.org/conferences/?conf=cbrkd2016">http://www.easychair.org/conferences/?conf=cbrkd2016</a></div><div><br></div><div>Organizing Committee</div><div><br></div><div>Co-Chairs</div><div><br></div><div>Isabelle Bichindaritz</div><div>State University of New York, Oswego</div><div>Oswego, NY, 13126, USA</div><div>Phone: +1 315 312 2683</div><div>Email: <a href="mailto:ibichind@oswego.edu">ibichind@oswego.edu</a></div><div><br></div><div>Cindy Marling</div><div>Ohio University </div><div>Athens, Ohio, 45701, USA</div><div>Phone: +1 740 593 1246</div><div>Email: <a href="mailto:marling@ohio.edu">marling@ohio.edu</a></div><div><br></div><div>Stefania Montani</div><div>University of Piemonte Orientale</div><div>I-15100 Alessandria, Italy </div><div>Phone: +30 0131 360158</div><div>Email: <a href="mailto:stefania.montani@unipmn.it">stefania.montani@unipmn.it</a></div><div><br></div><div><br></div></div>-- <br><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div> <span style="font-family:arial,helvetica,sans-serif"> Dr. Isabelle Bichindaritz</span></div><div><font face="arial, helvetica, sans-serif"> Associate Professor</font></div><div><font face="arial, helvetica, sans-serif"> Director of Biomedical Informatics</font></div><div><font face="arial, helvetica, sans-serif"> SUNY Oswego</font></div><div><font face="arial, helvetica, sans-serif"> Computer Science Department</font></div><div><font face="arial, helvetica, sans-serif"> Shineman 396A</font></div><div><font face="arial, helvetica, sans-serif"><span style="color:rgb(34,34,34);line-height:17.7778px;background-color:rgb(255,255,255)"> 7060 New York 104 Oswego, NY 13126</span></font></div><div><span style="color:rgb(34,34,34);line-height:17.7778px;background-color:rgb(255,255,255)"><font face="arial, helvetica, sans-serif"> USA</font></span></div><div><span style="color:rgb(34,34,34);line-height:17.7778px;background-color:rgb(255,255,255)"><font face="arial, helvetica, sans-serif"> <a href="http://cs.oswego.edu/~bichinda" target="_blank">http://cs.oswego.edu/~bichinda</a> </font></span></div><div><font face="arial, helvetica, sans-serif"> Ph: (315) 312 2683</font></div><div><font face="arial, helvetica, sans-serif"> Cell: (206) 455 0221</font></div><div><font face="arial, helvetica, sans-serif"> Email: <a href="mailto:ibichind@oswego.edu" target="_blank">ibichind@oswego.edu</a></font></div><div> </div></div></div></div></div></div></div></div></div></div></div></div></div>
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