<?xml version="1.0"?>

<!DOCTYPE owl [
  <!ENTITY rdf "http://www.w3.org/1999/02/22-rdf-syntax-ns#">
  <!ENTITY rdfs "http://www.w3.org/2000/01/rdf-schema#">
  <!ENTITY xsd "http://www.w3.org/2001/XMLSchema#">
  <!ENTITY owl "http://www.w3.org/2002/07/owl#">
  <!ENTITY cc "http://web.resource.org/cc/#">
  <!ENTITY event "http://ebiquity.umbc.edu/ontology/event.owl#">
  <!ENTITY person "http://ebiquity.umbc.edu/ontology/person.owl#">
  <!ENTITY assert "http://ebiquity.umbc.edu/ontology/assertion.owl#">]>

<!--
  This ontology document is licensed under the Creative Commons
  Attribution License. To view a copy of this license, visit
  http://creativecommons.org/licenses/by/2.0/ or send a letter to
  Creative Commons, 559 Nathan Abbott Way, Stanford, California
  94305, USA.
-->

<rdf:RDF 
  xmlns:rdf = "&rdf;"
  xmlns:rdfs = "&rdfs;"
  xmlns:xsd = "&xsd;"
  xmlns:owl = "&owl;"
  xmlns:cc = "&cc;"
  xmlns:event = "&event;"
  xmlns:person = "&person;"
  xmlns:assert = "&assert;">
  <event:Event rdf:about="http://ebiquity.umbc.edu/event/html/id/193/Knowledge-Transfer-using-Multiresolution-Learning">
    <rdfs:label><![CDATA[Knowledge Transfer using Multiresolution Learning]]></rdfs:label>
    <event:title><![CDATA[Knowledge Transfer using Multiresolution Learning]]></event:title>
    <event:speaker><person:Collaborator rdf:about="http://ebiquity.umbc.edu/person/html/Eric/Eaton/"><person:name><![CDATA[Eric  Eaton]]></person:name><rdfs:label><![CDATA[Eric  Eaton]]></rdfs:label></person:Collaborator></event:speaker>
    <event:startDate rdf:datatype="&xsd;dateTime">2007-03-07T15:30:00-05:00</event:startDate>
    <event:endDate rdf:datatype="&xsd;dateTime">2007-03-07T17:00:00-05:00</event:endDate>
    <event:location><![CDATA[325b]]></event:location>
    <event:abstract><![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.
<p>
The use of multiple resolutions allows the selective transfer of knowledge at specific levels of generalization between tasks. The proposed work focuses on two mechanisms for performing multiresolution transfer. The first method, data-based multiresolution transfer, uses multiple resolutions of input data to create models at different resolutions. The second method, model-based multiresolution transfer, generates multiple resolutions of previously learned models and then selectively transfers the appropriate resolution of the model. An additional contribution of this work will be a general framework for knowledge transfer that provides a foundation for comparing different transfer methods. ]]></event:abstract>
    <event:tag><![CDATA[learning]]></event:tag>
    <event:tag><![CDATA[proposal]]></event:tag>
    <event:tag><![CDATA[dissertation]]></event:tag>
    <event:host><person:Collaborator rdf:about="http://ebiquity.umbc.edu/person/html/Marie/desJardins/"><person:name><![CDATA[Marie  desJardins]]></person:name><rdfs:label><![CDATA[Marie  desJardins]]></rdfs:label></person:Collaborator></event:host>
  </event:Event>

  <rdf:Description rdf:about="">
    <cc:License rdf:resource="http://creativecommons.org/licenses/by/2.0/" />
  </rdf:Description>

</rdf:RDF>
