<rdf:RDF xmlns:community="http://www.bibsonomy.org/ontologies/2008/05/community#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:admin="http://webns.net/mvcb/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:cc="http://web.resource.org/cc/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:swrc="http://swrc.ontoware.org/ontology#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xml:base="http://www.bibsonomy.org/author/Drummond"><owl:Ontology rdf:about=""><rdfs:comment>BibSonomy publications for /author/Drummond</rdfs:comment><owl:imports rdf:resource="http://swrc.ontoware.org/ontology/portal"/></owl:Ontology><rdf:Description rdf:about="http://www.bibsonomy.org/bibtex/2db96a795ea99cc5c67c84556d7d0df89/dblp"><owl:sameAs rdf:resource="http://www.bibsonomy.org/uri/bibtex/2db96a795ea99cc5c67c84556d7d0df89/dblp"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#Article"/><owl:sameAs rdf:resource="http://dblp.uni-trier.de/db/journals/icl/icl12.html#DrummondF08"/><swrc:date>Fri Feb 03 00:00:00 CET 2012</swrc:date><swrc:journal>IEEE Communications Letters</swrc:journal><swrc:number>7</swrc:number><swrc:pages>523-525</swrc:pages><swrc:title>A Fixed-Parameter Tractable Approach for the Wavelength Assignment Problem in Transparent Networks.</swrc:title><swrc:volume>12</swrc:volume><swrc:year>2008</swrc:year><swrc:keywords>dblp </swrc:keywords><swrc:hasExtraField><swrc:Field swrc:value="http://dx.doi.org/10.1109/LCOMM.2008.080450" swrc:key="ee"/></swrc:hasExtraField><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="André C. Drummond"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Nelson L. S. da Fonseca"/></rdf:_2></rdf:Seq></swrc:author></rdf:Description><rdf:Description rdf:about="http://www.bibsonomy.org/bibtex/2dc8ebafcf3ff94d41a8099d6ad0c2df2/gron"><owl:sameAs rdf:resource="http://www.bibsonomy.org/uri/bibtex/2dc8ebafcf3ff94d41a8099d6ad0c2df2/gron"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#Misc"/><owl:sameAs rdf:resource="http://www.site.uottawa.ca/ICML09WS/papers/w2.pdf"/><swrc:date>Thu Jan 05 12:37:49 CET 2012</swrc:date><swrc:address>Montreal, Canada</swrc:address><swrc:booktitle>The 4th workshop on Evaluation Methods for Machine Learning</swrc:booktitle><swrc:month>06/2009</swrc:month><swrc:title>Replicability in not Reproducibility: Nor is it Good Science</swrc:title><swrc:year>2009</swrc:year><swrc:keywords>Experiments Replicability Science Value </swrc:keywords><swrc:abstract>At various machine learning conferences, at various times, there have been discussions arising from the inability to replicate the experimental result published in a paper. There seems to be a wide spread view that we need to do something to address this problem, as it is essential to the advancement of our field. The most compelling argument would seem to be that reproducibility of experimental results is the hallmark of science. Therefore, given that most of us regard machine learning as a scientific discipline, being able to replicate experiments is paramount. I want to challenge this view by separating the notion of reproducibility, a generally desirable property, from replicability, its poor cousin. I claim there are important differences between the two. Reproducibility requires changes; replicability avoids them. 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