http://www.bibsonomy.org/burst/user/sb3000/mlBibSonomy BuRST Feed for /user/sb3000/ml2008-05-14T10:05:01+02:00http://www.bibsonomy.org/bibtex/22de98c2b635f36c137e25256e8c235e0/sb3000sb30002008-04-25T14:59:33+02:00ir ml sb sb-editor <span style="color:#555555;">Stephan <a href="http://www.bibsonomy.org/author/Bloehdorn">Bloehdorn</a> und Wray <a href="http://www.bibsonomy.org/author/Buntine">Buntine</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> (eds.). </span>(<em>2005</em>) <em>Workshop at the 22nd International Conference on Machine Learning (ICML 2005) . </em>Workshop at the 22nd International Conference on Machine Learning (ICML 2005) Proceedings of the Workshop on Learning in Web Search (LWS 2005) 2005irmlsbsb-editorhttp://www.bibsonomy.org/bibtex/20b70a74881cbc955d2312a9ef2ad7871/sb3000sb30002008-03-31T14:29:41+02:00ml propositionalization structured-data <span style="color:#555555;">Chad M. <a href="http://www.bibsonomy.org/author/Cumby">Cumby</a> und Dan <a href="http://www.bibsonomy.org/author/Roth">Roth</a> </span><em>Proceedings of the 12th International Conference on Inductive Logic Programming (ILP 2002), July 9 - 11, 2002, Sydney, Australia --- Revised Papers, </em><em>2583, </em><em>Seite32--47. </em><em>Springer, Berlin--Heidelberg, Germany, </em>(<em>2003</em>)Proceedings of the 12th International Conference on Inductive Logic Programming (ILP 2002), July 9 - 11, 2002, Sydney, Australia --- Revised Papers258332--47Lecture Notes in Computer ScienceLearning with Feature Description Logics2003mlpropositionalizationstructured-datahttp://www.bibsonomy.org/bibtex/2502a6bf8f280f1c850a46cece43cc7a7/sb3000sb30002008-03-28T12:37:19+01:00logic ml <span style="color:#555555;">J.W. <a href="http://www.bibsonomy.org/author/Lloyd">Lloyd</a> </span><em>Springer, </em><em>Berlin--Heidelberg, Germany, </em>(<em>2003</em>)Berlin--Heidelberg, GermanyLogic for Learning: Learning Comprehensible Theories from Structured Data2003logicmlhttp://www.bibsonomy.org/bibtex/21d17b9d54a4ef23864227f647622d071/sb3000sb30002008-03-14T12:12:21+01:00classification kernels ml svm <span style="color:#555555;">John <a href="http://www.bibsonomy.org/author/Shawe-Taylor">Shawe-Taylor</a> und Nello <a href="http://www.bibsonomy.org/author/Cristianini">Cristianini</a> </span><em>Cambridge University Press, </em><em>Cambridge, UK, </em><em>June2004. </em>Cambridge, UKJuneKernel Methods for Pattern Analysis2004classificationkernelsmlsvmhttp://www.bibsonomy.org/bibtex/2251b00a05b3df11168de911d7591490e/sb3000sb30002008-03-14T12:11:30+01:00kernels ml svm <span style="color:#555555;">Nello <a href="http://www.bibsonomy.org/author/Cristianini">Cristianini</a> und John <a href="http://www.bibsonomy.org/author/Shawe-Taylor">Shawe-Taylor</a> </span><em>Cambridge University Press, </em><em>Cambridge, UK, </em><em>March2000. </em>Cambridge, UKHardcoverMarchAn Introduction to Support Vector Machines and Other Kernel-based Learning Methods2000kernelsmlsvmhttp://www.bibsonomy.org/bibtex/26abee7d4dbc7874815f8d18f20d3e3a9/sb3000sb30002008-03-10T16:07:01+01:00DL ml <span style="color:#555555;">William W. <a href="http://www.bibsonomy.org/author/Cohen">Cohen</a> und Haym <a href="http://www.bibsonomy.org/author/Hirsh">Hirsh</a> </span><em>Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning (KR'94), May 24-27, 1994, Bonn, Germany, </em><em>Seite121--133. </em><em>Morgan-Kauffman Publishers, San Francisco, CA, USA, </em>(<em>1994</em>)Proceedings of the 4th International Conference on Principles of Knowledge Representation and Reasoning (KR&#039;94), May 24-27, 1994, Bonn, Germany121--133Learning the {CLASSIC} Description Logic: Theoretical and Experimental Results1994DLmlhttp://www.bibsonomy.org/bibtex/2551d12bdef0fa1335c473317cfa4575e/sb3000sb30002008-03-10T14:30:14+01:00ml ontologies <span style="color:#555555;">Stephan <a href="http://www.bibsonomy.org/author/Bloehdorn">Bloehdorn</a> und Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> </span><em>Handbook on Ontologies, </em><em>chapter 31, </em><em>Springer, </em><em>Berlin--Heidelberg, Germany, </em><em>Second Edition, </em><em>To appear. . </em>(<em>2008</em>)Berlin--Heidelberg, GermanyHandbook on Ontologies 31SecondTo appear.Machine Learning and Ontologies2008mlontologieshttp://www.bibsonomy.org/bibtex/2f138d4d67e0bed94bd3c726af83c0492/sb3000sb30002008-02-12T18:07:08+01:00algorithms ml seminal <span style="color:#555555;">T. <a href="http://www.bibsonomy.org/author/Cover">Cover</a> und P. <a href="http://www.bibsonomy.org/author/Hart">Hart</a> </span><em>IEEE Transactions on Information Theory</em>(<em>1967</em>)IEEE Transactions on Information Theory21- 27Nearest neighbor pattern classification131967algorithmsmlseminalThe nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of errorRof such a rule must be at least as great as the Bayes probability of errorR^{ast}--the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in theM-category case thatR^{ast} leq R leq R^{ast}(2 --MR^{ast}/(M-1)), where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.http://www.bibsonomy.org/bibtex/24ea6707ab6b5ac055fb09d47ba195e02/sb3000sb30002007-11-22T14:57:54+01:00ml ontology similarity <span style="color:#555555;">Floriana <a href="http://www.bibsonomy.org/author/Esposito">Esposito</a> und Nicola <a href="http://www.bibsonomy.org/author/Fanizzi">Fanizzi</a> und Luigi <a href="http://www.bibsonomy.org/author/Iannone">Iannone</a> und Ignazio <a href="http://www.bibsonomy.org/author/Palmisano">Palmisano</a> und Giovanni <a href="http://www.bibsonomy.org/author/Semeraro">Semeraro</a> </span><em>The Semantic Web - ISWC 2004: Third International Semantic, </em><em>Volume3298vonLecture Notes in Computer Science, </em><em>Seite441-455. </em><em>Springer, </em>(<em>2004</em>)The Semantic Web - ISWC 2004: Third International Semantic441-455Lecture Notes in Computer ScienceKnowledge-Intensive Induction of Terminologies from Metadata32982004mlontologysimilarityMy Main bibliography filehttp://www.bibsonomy.org/bibtex/2c7155e97d2e7549e418251d0f9ad7ca9/sb3000sb30002007-10-24T11:19:31+02:00ml semweb <span style="color:#555555;">P. <a href="http://www.bibsonomy.org/author/Edwards">Edwards</a> und G. AA. <a href="http://www.bibsonomy.org/author/Grimnes">Grimnes</a> und A. <a href="http://www.bibsonomy.org/author/Preece">Preece</a> </span><em>ECML/PKDD, Semantic Web Mining Workshop, </em><em>Seite71-89. </em>(<em>2002</em>)ECML/PKDD, Semantic Web Mining Workshop71-89An Empirical Investigation of Learning from the Semantic Web2002mlsemweb