BibSonomyburst/BibSonomy RSS feed for /2012-02-14T01:27:41+01:00http://www.bibsonomy.org/bibtex/2268e9546446d8bd65e0a15792879e333/folkefolke2012-02-14T00:56:20+01:00analysis entropy graph network theory <span class="authorEditorList"><a href="/author/Simonyi">G. Simonyi</a> </span><em>Combinatorial Optimization</em> (<em>1995</em>)Tue Feb 14 00:56:20 CET 2012Combinatorial Optimization399--441Graph entropy: A survey201995analysis entropy graph network theory http://www.bibsonomy.org/bibtex/27f9e0de989ed9bd4deaa41d39bc98d4c/folkefolke2012-02-14T00:53:13+01:00analysis entropy graph network theory <span class="authorEditorList"><a href="/author/Dehmer">M. Dehmer</a> </span><em>Applied Mathematics and Computation</em> <em>201(1):82--94</em> (<em>2008</em>)Tue Feb 14 00:53:13 CET 2012Applied Mathematics and Computation182--94Information processing in complex networks: Graph entropy and information functionals2012008analysis entropy graph network theory http://www.bibsonomy.org/bibtex/2df6dbed89a49a368270d4ea42beebc26/drmatusekdrmatusek2012-02-14T00:52:33+01:00chemistry cluster coupled quantum review theory <span class="authorEditorList"><a href="/author/Lyakh">Dmitry I. Lyakh</a>, <a href="/author/Musial">Monika Musial</a>, <a href="/author/Lotrich">Victor F. Lotrich</a>, and <a href="/author/Bartlett">Rodney J. Bartlett</a> </span><em>Chemical Reviews</em> <em>112(1):182-243</em> (<em>January 2012</em>)Tue Feb 14 00:52:33 CET 2012Chemical ReviewsJanuary1182-243Multireference Nature of Chemistry: The Coupled-Cluster View1122012chemistry cluster coupled quantum review theory A review of advances in multireference coupled cluster theory and methods. Section titles are: Introduction; Exponential Era of Electron Correlation Theory; Genuine Multireference Coupled-Cluster Theory in Hilbert Space; Genuine Multireference Coupled-Cluster Theory in Fock Space; Alternative Multireference Coupled-Cluster Methods; Numerical Illustrations; To Multireference or Not to Multireference?http://www.bibsonomy.org/bibtex/2000eaa395ab34d01885bf70fbd9cd1ac/beatebeate2012-02-13T23:20:51+01:00challenges spam spam-detection survey <span class="authorEditorList"><a href="/author/Fawcett">Tom Fawcett</a> </span><em>SIGKDD Explor. Newsl.</em> (<em>December 2003</em>)Mon Feb 13 23:20:51 CET 2012New York, NY, USASIGKDD Explor. Newsl.December140--148"In vivo" spam filtering: a challenge problem for KDD52003challenges spam spam-detection survey Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by treating it as a static text classification problem. True <i>in vivo</i> spam filtering has characteristics that make it a rich and challenging domain for data mining. Indeed, real-world datasets with these characteristics are typically difficult to acquire and to share. This paper demonstrates some of these characteristics and argues that researchers should pursue <i>in vivo</i> spam filtering as an accessible domain for investigating them."In vivo" spam filteringhttp://www.bibsonomy.org/bibtex/2275220e7853ed9478bf88e804faf96a2/beatebeate2012-02-13T22:45:00+01:00ml naive-bayes spam spam-detection survey <span class="authorEditorList"><a href="/author/Guzella">Thiago S. Guzella</a>, and <a href="/author/Caminhas">Walmir M. Caminhas</a> </span><em>Expert Syst. Appl.</em> (<em>September 2009</em>)Mon Feb 13 22:45:00 CET 2012Tarrytown, NY, USAExpert Syst. Appl.September10206--10222Review: A review of machine learning approaches to Spam filtering362009ml naive-bayes spam spam-detection survey In this paper, we present a comprehensive review of recent developments in the application of machine learning algorithms to Spam filtering, focusing on both textual- and image-based approaches. Instead of considering Spam filtering as a standard classification problem, we highlight the importance of considering specific characteristics of the problem, especially concept drift, in designing new filters. Two particularly important aspects not widely recognized in the literature are discussed: the difficulties in updating a classifier based on the bag-of-words representation and a major difference between two early naive Bayes models. Overall, we conclude that while important advancements have been made in the last years, several aspects remain to be explored, especially under more realistic evaluation settings. Review