{"id":31,"date":"2010-08-27T18:48:11","date_gmt":"2010-08-27T16:48:11","guid":{"rendered":"http:\/\/lwibs01.gm.fh-koeln.de\/blogs\/ciop\/?page_id=31"},"modified":"2025-01-20T09:12:22","modified_gmt":"2025-01-20T08:12:22","slug":"soma","status":"publish","type":"page","link":"https:\/\/blogs.gm.fh-koeln.de\/ciop\/research\/soma\/","title":{"rendered":"SOMA"},"content":{"rendered":"
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\n\t\t\"logo_soma\"<\/a>S<\/strong>ystematical O<\/strong>ptimization of M<\/strong>odels in A<\/strong>utomation a<\/span>nd <\/span>IT
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\n\tThis BMBF-sponsored project was conducted in 2009-2013. Project partner were Leiden U<\/strong><\/span>niversity<\/strong>, NL, University Bochum<\/strong>, Nurogames GmbH<\/strong>, Cologne and  divis GmbH<\/strong>, Dortmund.<\/span>  \n<\/div>\n
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\n\t\t\"BMBF_Logo\"<\/a>
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\n\tSystematic optimization of models for complex applications in IT and automation, here with the goal of forecasting and optimal control of plants or processes, is the topic of this project. It poses still a great challenge for the practioner in computer science or engineering. In many cases it is not alone a problem of the right model parametrization, but also a task of intelligent data preprocessing and data selection. Here the project SOMA aims at developing and offering new solutions and tools.<\/span>\n<\/div>\n
\n\t \n<\/div>\n
\n\tUnder the umbrella of SOMA several sub-projects were undertaken:  Tuned data mining (TDMR)<\/a>, gesture recognition<\/a>, reinforcement learning for strategic games, intelligent methods for feature generation like slow feature analysis (SFA)<\/a> or n-tuple systems.  <\/span>\n<\/div>\n
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\n\tTopics<\/strong>: Applied computer science, modeling, simulation, learning system, computational intelligence (evolutionary algorithms, neural networks), data mining.<\/span>\n<\/div>\n
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\n\t\tPublications SOMA<\/span><\/strong>\n\t<\/p>\n

<\/a><\/form>

2018<\/h3>

Konen, Wolfgang; Koch, Patrick<\/p>

The TDMR 2.0 Package: Tuned Data Mining in R<\/a> Forschungsbericht<\/span> <\/p>

Research Center CIOP (Computational Intelligence, Optimization and Data Mining) <\/span>Cologne University of Applied Science, Faculty of Computer Science and Engineering Science, <\/span>Nr. 02\/2018, <\/span>2018<\/span>, (Last update: April 2018 (original version: 2012))<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@techreport{Kone18a,
\r\ntitle = {The TDMR 2.0 Package: Tuned Data Mining in R},
\r\nauthor = {Wolfgang Konen and Patrick Koch},
\r\nurl = {http:\/\/www.gm.fh-koeln.de\/ciopwebpub\/Kone12a.d\/Kone12a.pdf},
\r\nyear  = {2018},
\r\ndate = {2018-04-01},
\r\nnumber = {02\/2018},
\r\naddress = {Cologne University of Applied Science, Faculty of Computer Science and Engineering Science},
\r\ninstitution = {Research Center CIOP (Computational Intelligence, Optimization and Data Mining)},
\r\nnote = {Last update: April 2018 (original version: 2012)},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {techreport}
\r\n}
\r\n<\/pre><\/div>
Schlie\u00dfen<\/a><\/p><\/div>