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    <rdf:li resource="http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks"/>
    <rdf:li resource="http://ebiquity.umbc.edu/event/html/id/193/Knowledge-Transfer-using-Multiresolution-Learning"/>
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 <item rdf:about="http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks">
  <title><![CDATA[Semantically-Linked Bayesian Networks]]></title>
  <link>http://ebiquity.umbc.edu/resource/html/id/194/Semantically-Linked-Bayesian-Networks</link>
  <description><![CDATA[At the present time, Bayesian networks (BNs), presumably the most popular uncertainty inference framework, are still widely used as standalone systems. When the problem itself is distributed, domain knowledge has to be centralized and unified before a single BN can be created. Alternatively, separate BNs describing related sub-domains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving even if the interdependent relations between var...]]></description>
  <dc:date>2006-08-02</dc:date>
 </item>
 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks">
  <title><![CDATA[Probabilistic Approximate Algorithms for Distributed Data Mining in Peer-to-Peer Networks]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/238/Probabilistic-Approximate-Algorithms-for-Distributed-Data-Mining-in-Peer-to-Peer-Networks</link>
  <description><![CDATA[Peer-to-peer(P2P) computing is emerging as a new distributed computing 
paradigm for novel applications that involves exchange of information 
among  peers with little centralized coordination. Analyzing data 
distributed in P2P networks requires peer-to-peer data mining algorithms 
that can mine the data without data centralization. However, 
replicating  result of centralized data mining in an exact fashion is 
often communication-wise expensive. Approximate algorithms can be a 
real...]]></description>
  <dc:date>2008-04-28</dc:date>
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 <item rdf:about="http://ebiquity.umbc.edu/event/html/id/193/Knowledge-Transfer-using-Multiresolution-Learning">
  <title><![CDATA[Knowledge Transfer using Multiresolution Learning]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/193/Knowledge-Transfer-using-Multiresolution-Learning</link>
  <description><![CDATA[For my dissertation research, I propose to explore the transfer of knowledge at multiple levels of abstraction to improve learning. These multiple levels of abstraction will be created using multiresolution analysis, providing a principled and formal mechanism for abstracting knowledge.  I claim that by exploiting the similarities between objects at various levels of detail, learning at multiple resolutions can facilitate transfer between related tasks.

The use of multiple resolutions allo...]]></description>
  <dc:date>2007-03-07</dc:date>
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  <title><![CDATA[Enhancing Semantic Web Data Access]]></title>
  <link>http://ebiquity.umbc.edu/event/html/id/142/Enhancing-Semantic-Web-Data-Access</link>
  <description><![CDATA[The Semantic Web is coined by Tim Berners-Lee in 1998 as a web of data
for machine consumption. Its applicability in supporting real world
applications on the World Wide Web, however, remains unclear to this day
mainly because its Web aspect has been neglected in past research.  Most
existing works usually model the Semantic Web as one universal RDF
graph, and they either ignore the storage layer or investigate ad hoc
storage layers (e.g. databases and peer-to-peer systems) other than t...]]></description>
  <dc:date>2006-04-06</dc:date>
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