BibSonomyburst/user/pitman/mlBibSonomy RSS feed for /user/pitman/ml2012-02-15T20:13:44+01:00http://www.bibsonomy.org/bibtex/24ce9a832df16ba8a0c876b9c71c8461d/pitmanpitman2008-01-15T05:49:09+01:00classification ml <span class="authorEditorList"><a href="/author/Sandler">Mark Sandler</a> </span><em>KDD &#039;07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, </em><em>page 580--589. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Tue Jan 15 05:49:09 CET 2008New York, NY, USAKDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining580--589Hierarchical mixture models: a probabilistic analysis2007classification ml Hierarchical mixture modelshttp://www.bibsonomy.org/bibtex/2e1c6f29f7a54860167cbfd035f31eb39/pitmanpitman2008-01-15T01:27:53+01:00algorithmic learning ml theory <span class="authorEditorList"><a href="/author/Abe">N. Abe</a>, and <a href="/author/Khardon">R. Khardon</a> (Eds.). </span><em>Elsevier Science Publishers B.V., </em><em>Amsterdam, </em>(<em>2004</em>)<em>Papers from the 12th Annual Conference ALT&#039;01 held in Washington, DC, November 25--28, 2001, Theoret. Comput. Sci. \bf 313 2004, no. 2 . </em>Tue Jan 15 01:27:53 CET 2008AmsterdamPapers from the 12th Annual Conference (ALT'01) held in Washington, DC, November 25--28, 2001, Theoret. Comput. Sci. {\bf 313} (2004), no. 2i--iv and 173--Algorithmic learning theory2004algorithmic learning ml theory MR lookuphttp://www.bibsonomy.org/bibtex/2624d2a29008c475245aa0bca052e7c0f/pitmanpitman2008-01-09T01:08:56+01:00ml <span class="authorEditorList"><a href="/author/Song">Yang Song</a>, <a href="/author/Huang">Jian Huang</a>, <a href="/author/Councill">Isaac G. Councill</a>, <a href="/author/Li">Jia Li</a>, and <a href="/author/Giles">C. Lee Giles</a> </span><em>JCDL &#039;07: Proceedings of the 2007 conference on Digital libraries, </em><em>page 342--351. </em><em>New York, NY, USA, </em><em>ACM, </em>(<em>2007</em>)Wed Jan 09 01:08:56 CET 2008New York, NY, USAJCDL '07: Proceedings of the 2007 conference on Digital libraries342--351Efficient topic-based unsupervised name disambiguation2007ml Name ambiguity is a special case of identity uncertainty where one person can be referenced by multiple name variations in different situations or even share the same name with other people. In this paper, we focus on the problem of disambiguating person names within web pages and scientific documents. We present an efficient and effective two-stage approach to disambiguate names. In the first stage, two novel topic-based models are proposed by extending two hierarchical Bayesian text models, namely Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). Our models explicitly introduce a new variable for persons and learn the distribution of topics with regard to persons and words. After learning an initial model, the topic distributions are treated as feature sets and names are disambiguated by leveraging a hierarchical agglomerative clustering method. Experiments on web data and scientific documents from CiteSeer indicate that our approach consistently outperforms other unsupervised learning methods such as spectral clustering and DBSCAN clustering and could be extended to other research fields. We empirically addressed the issue of scalability by disambiguating authors in over 750,000 papers from the entire CiteSeer dataset. Efficient topic-based unsupervised name disambiguationhttp://www.bibsonomy.org/bibtex/2b2e5eae7c1cc273831d431b2c833f835/pitmanpitman2008-01-09T00:18:12+01:00ml <span class="authorEditorList"><a href="/author/et al">C.L. Giles et al</a> </span>(<em>2008</em>)Wed Jan 09 00:18:12 CET 2008Generating Summary Keywords for Emails Using Topics2008ml