PUMA publications for /tag/learning%20mininghttps://puma.uni-kassel.de/tag/learning%20miningPUMA RSS feed for /tag/learning%20mining2024-03-29T03:26:09+01:00Resource-Aware On-Line RFID Localization Using Proximity Datahttps://puma.uni-kassel.de/bibtex/2c1614b434eb13f0f42884ccffae8141d/itegiteg2011-11-22T10:26:32+01:002011 data itegpub learning localization machine mining myown rfid <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Christoph Scholz" itemprop="url" href="/author/Christoph%20Scholz"><span itemprop="name">C. Scholz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Atzmueller" itemprop="url" href="/author/Martin%20Atzmueller"><span itemprop="name">M. Atzmueller</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"></span>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Tue Nov 22 10:26:32 CET 2011Resource-Aware On-Line RFID Localization Using Proximity Data20112011 data itegpub learning localization machine mining myown rfid Incorporating Exceptions: Efficient Mining of epsilon-Relevant Subgroup Patternshttps://puma.uni-kassel.de/bibtex/20c6a9543bb5381a57baf8837db7b4249/atzmuelleratzmueller2010-05-03T12:25:20+02:00data exceptions learning mining myown relevancy subgroup-discovery <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Florian Lemmerich" itemprop="url" href="/author/Florian%20Lemmerich"><span itemprop="name">F. Lemmerich</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Martin Atzmueller" itemprop="url" href="/author/Martin%20Atzmueller"><span itemprop="name">M. Atzmueller</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases</span>, </em></span>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)<em>accepted.</em>Mon May 03 12:25:20 CEST 2010Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databasesaccepted{Incorporating Exceptions: Efficient Mining of epsilon-Relevant Subgroup Patterns}2009data exceptions learning mining myown relevancy subgroup-discovery Reconciling schemas of disparate data sources: a machine-learning approachhttps://puma.uni-kassel.de/bibtex/229e7660361ca79b97b00e5db51fb66ee/hothohotho2010-02-07T11:17:31+01:00data learning mapping mining ol schema <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="AnHai Doan" itemprop="url" href="/author/AnHai%20Doan"><span itemprop="name">A. Doan</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pedro Domingos" itemprop="url" href="/author/Pedro%20Domingos"><span itemprop="name">P. Domingos</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alon Y. Halevy" itemprop="url" href="/author/Alon%20Y.%20Halevy"><span itemprop="name">A. Halevy</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>SIGMOD Rec.</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">30 </span></span>(<span itemprop="issueNumber">2</span>):
<span itemprop="pagination">509--520</span></em> </span>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Sun Feb 07 11:17:31 CET 2010New York, NY, USASIGMOD Rec.2509--520Reconciling schemas of disparate data sources: a machine-learning approach302001data learning mapping mining ol schema A data-integration system provides access to a multitude of data sources through a single mediated schema. A key bottleneck in building such systems has been the laborious manual construction of semantic mappings between the source schemas and the mediated schema. We describe LSD, a system that employs and extends current machine-learning techniques to semi-automatically find such mappings. LSD first asks the user to provide the semantic mappings for a small set of data sources, then uses these mappings together with the sources to train a set of learners. Each learner exploits a different type of information either in the source schemas or in their data. Once the learners have been trained, LSD finds semantic mappings for a new data source by applying the learners, then combining their predictions using a meta-learner. To further improve matching accuracy, we extend machine learning techniques so that LSD can incorporate domain constraints as an additional source of knowledge, and develop a novel learner that utilizes the structural information in XML documents. Our approach thus is distinguished in that it incorporates multiple types of knowledge. Importantly, its architecture is extensible to additional learners that may exploit new kinds of information. We describe a set of experiments on several real-world domains, and show that LSD proposes semantic mappings with a high degree of accuracy.UCI Repository of machine learning databaseshttps://puma.uni-kassel.de/bibtex/285308db3df761f63f16a7cab4eb8d4aa/hothohotho2006-06-23T07:06:19+02:00learning data dataset dm mining machine ml uci <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C.L. Blake D.J. Newman" itemprop="url" href="/author/C.L.%20Blake%20D.J.%20Newman"><span itemprop="name">C. Newman</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="C.J. Merz" itemprop="url" href="/author/C.J.%20Merz"><span itemprop="name">C. Merz</span></a></span>. </span>(<em><span>1998<meta content="1998" itemprop="datePublished"/></span></em>)Fri Jun 23 07:06:19 CEST 2006{UCI} Repository of machine learning databases1998learning data dataset dm mining machine ml uci UCI Machine Learning Repository