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Meetings/Workshops on Information Theory, Foundations of Computer Science in the United States (USA)
Conference-Service.com offers, as part of our business activities, a directory of upcoming scientific and technical meetings. The calendar is published for the convenience of conference participants and we strive to support conference organisers who need to publish their upcoming events.
Although great care is being taken to ensure the correctness of all entries, we cannot accept any liability that may arise from the presence, absence or incorrectness of any particular information on this website. Always check with the meeting organiser before making arrangements to participate in an event!
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|1.LICS 2013 — 2013 Twenty-Eighth Annual IEEE/ACM Symposium on Logic in Computer Science|
| Dates ||25 Jun 2013 → 28 Jun 2013 |
| Location||New Orleans, LA, United States |
| Abstract ||The LICS Symposium is an annual international forum on theoretical and practical topics in computer science that relate to logic, broadly construed. |
| Weblink ||http://lii.rwth-aachen.de/lics/lics13/|
|3.WAIFDM — DIMACS Workshop on Algorithmic Information Fusion and Data Mining |
| Dates ||19 Sep 2013 → 20 Sep 2013 |
| Location||DIMACS Center, CoRE Building, Rutgers University, , United States |
| Abstract ||Presented under the auspices of the Special Focus on Algorithmic Decision Theory and in partnership with the European Consortium ALGODEC. Information fusion and data mining are fundamental in the scientific discovery process of data acquisition, information integration, and knowledge discovery. Although methods for information fusion and data mining have been used for hundreds of years, it remains a challenging problem to understand when, what, and how to optimally mine data, fuse information and discover knowledge. Among others, the DIMACS Workshop on Algorithmic Information Fusion and Data Mining (WAIFDM) will address the following two types of problems: Given a complex problem in a data-rich environment, how to extract variables and how to perform variable selection and combination? Here 'variable' includes feature, attribute, cue, indicator, and parameter. Given two machine learning or data mining systems A and B, when and how to best combine A and B? Given many possible decisions systems for a solution, how to best select and combine a subset of these systems?|
| Weblink ||http://dimacs.rutgers.edu/Workshops/InformationFusion/index.html|
| Related subject(s) ||Algorithms|
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Last updated: 02 June 2013