Publications
2008
T. Joachims and T. Finley and Chun-Nam Yu,Cutting-Plane Training of Structural SVMs, Machine Learning, to appear.
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T. Finley and T. Joachims, Training Structural SVMs when Exact Inference isIntractable,Proceedings of theInternational Conference on Machine Learning (ICML), 2008.
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Yisong Yue and T. Joachims, Predicting Diverse Subsets Using StructuralSVMs,Proceedings of theInternational Conference on Machine Learning (ICML), 2008.
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F. Radlinskiand R. Kleinberg and T. Joachims,Learning Diverse Rankings with Multi-Armed Bandits,Proceedings of theInternational Conference on Machine Learning (ICML), 2008.
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2007
T. Joachims, F. Radlinski,Search Engines that Learn from Implicit Feedback,IEEE Computer, Vol. 40, No. 8,August, 2007.
[IEEEDigital Library][BibTeX][Software]
B. Shaparenko,T. Joachims, Information Genealogy: Uncovering the Flow of Ideas inNon-Hyperlinked Document Databases,Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM,2007.
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F. Radlinski,T. Joachims, Active Exploration for Learning Rankings from ClickthroughData,Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM,2007.
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T. Finley,T. Joachims, Parameter Learning for Loopy Markov Random Fields withStructural Support Vector Machines,ICML Workshop on Constrained Optimization and Structured Output Spaces,2007.
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Yisong Yue, T. Finley, F.Radlinski,T. Joachims, A Support Vector Method for Optimizing Average Precision,Proceedings of theConference on Research and Development in Information Retrieval (SIGIR),2007.
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Chun-Nam Yu,T. Joachims, R. Elber, J. Pillardy, Support Vector Training of ProteinAlignment Models,Proceeding of the International Conference on Research in ComputationalMolecular Biology (RECOMB),2007.
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S. Pohl,F. Radlinski, T. Joachims, Recommending Related Papers Based on DigitalLibrary Access Records,Proceeding of the Joint Conference on Digital Libraries (JCDL),2007.
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T. Joachims, L. Granka, Bing Pan, H. Hembrooke, F. Radlinski, G. Gay, Evaluating the Accuracy of Implicit Feedback from Clicks and QueryReformulations in Web Search,ACM Transactions on Information Systems (TOIS),Vol. 25, No. 2 (April), 2007.
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C. Domshlakand T. Joachims, Efficient and Non-Parametric Reasoning over UserPreferences, User Modeling and User-Adapted Interaction (UMUAI), Vol. 17,No. 1-2, pp. 41-69, Springer,2007.
[Springer Link][BibTeX]
2006
Best Research Paper AwardT. Joachims, Training Linear SVMs in Linear Time,Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM,2006.
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F. Radlinski and T. Joachims, Minimally Invasive Randomization forCollecting Unbiased Preferences from Clickthrough Logs,Proceedings of the National Conference of the American Association forArtificial Intelligence (AAAI),2005.
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Chun-Nam Yu,T. Joachims, and R. Elber, Training Protein Threading Models Using Structural SVMs,ICML Workshop on Learning in Structured Output Spaces,2006.
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2005
B. Shaparenko, R. Caruana, J. Gehrke, and T.Joachims, Identifying Temporal Patterns and Key Players in DocumentCollections. Proceedings of the IEEE ICDMWorkshop on Temporal Data Mining: Algorithms, Theory and Applications(TDM-05), pp. 165–174, 2005.
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Best Paper AwardT. Joachims, A Support Vector Method for Multivariate Performance Measures,Proceedings of theInternational Conference on Machine Learning (ICML), 2005.
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T. Joachims and J. Hopcroft, Error Bounds for Correlation Clustering, Proceedings of theInternational Conference on Machine Learning (ICML), 2005.
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Outstanding Student Paper AwardT. Finley and T. Joachims, Supervised Clustering with Support VectorMachines,Proceedings of theInternational Conference on Machine Learning (ICML), 2005.
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T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, Accurately Interpreting Clickthrough Data as Implicit Feedback, Proceedings of the Conference on Research and Development in Information Retrieval (SIGIR), 2005.
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Best Student Paper AwardF. Radlinski and T. Joachims, Query Chains: Learning to Rank from ImplicitFeedback,Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM,2005.
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F. Radlinski and T.Joachims, Evaluating the Robustness of Learning from Implicit Feedback,ICML Workshop on Learning In Web Search, 2005.
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C. Domshlakand T. Joachims, Unstructuring User Preferences: Efficient Non-ParametricUtility Revelation, Proceedings of the Conference on Uncertainty inArtificial Intelligence (UAI), 2005.
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T. Joachims, T. Galor, and R. Elber, Learning to Align Sequences: AMaximum-MarginApproach, In: New Algorithms forMacromolecular Simulation,B. Leimkuhler, LNCS Vol. 49, Springer, 2005.
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I.Tsochantaridis, T. Joachims,T. Hofmann,and Y. Altun,Large Margin Methods for Structured and Interdependent OutputVariables, Journal ofMachine Learning Research (JMLR),6(Sep):1453-1484, 2005.
[PDF] [BibTeX][Software] 2004
I.Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, Support Vector Machine Learning forInterdependent and Structured Output Spaces, Proceedings of the International Conference on Machine Learning (ICML), 2004.
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L. Granka, T. Joachims, andG. Gay, Eye-Tracking Analysis of User Behavior in WWW-Search, Poster Abstract, Proceedings of the Conference on Research and Development inInformation Retrieval (SIGIR), 2004.
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R. Caruana, T. Joachims, and L. Backstrom. KDDCup 2004: Results and Analysis, ACM SIGKDD Newsletter, 6(2):95-108,2004.
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P. Ginsparg, P. Houle, T. Joachims, andJ.-H. Sul, Mapping Subsets of Scholarly Information, Proceedings of theNational Academy of Sciences of the USA, 10.1073, Vol. 101, pages 5236-5240, 2004.
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2003
M. Schultz and T. Joachims, Learninga Distance Metric from Relative Comparisons, Proceedings of the Conferenceon Advance in Neural Information Processing Systems (NIPS), 2003.
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T. Joachims, TransductiveLearning via Spectral Graph Partitioning,Proceedings of the International Conference on Machine Learning (ICML), 2003.
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T. Joachims, Learning to AlignSequences: A Maximum-Margin Approach, Technical Report, August, 2003.
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T. Joachims, EvaluatingRetrieval Performance Using Clickthrough Data, in J. Franke and G.Nakhaeizadeh and I. Renz, "Text Mining", Physica/Springer Verlag, pp. 79-96,2003.2002
T. Joachims, Learningto Classify Text using Support Vector Machines, Dissertation, Kluwer, 2002.
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T. Joachims, EvaluatingRetrieval Performance Using Clickthrough Data, Proceedings of the SIGIRWorkshop on Mathematical/Formal Methods in Information Retrieval, 2002.
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T. Joachims, Optimizing Search Engines Using Clickthrough Data, Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD), ACM, 2002.
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T. Joachims, The Maximum-Margin Approach to Learning Text Classifiers, Ausgezeichnete Informatikdissertationen 2001, D. Wagner et al. (Hrsg.), GI-Edition - Lecture Notes in Informatics (LNI), Köllen Verlag, Bonn, 2002.
P. Sengers, R. Liesendahl,W. Magar, C. Seibert, B. Mueller, T. Joachims, W. Geng, P. Martensson, and K.Hook, The Enigmatics of Affect, Proceedings of the Conference onDesigning Interactive Systems (DIS), 2002.
2001
S. Wrobel, K. Morik, and T. Joachims,Maschinelles Lernen und Data Mining in: G. Görz, C. Rollinger, J.Schneeberger, Handbuch der künstlichen Intelligenz, Oldenburg, 2001.
T. Joachims, AStatistical Learning Model of Text Classification with Support VectorMachines. Proceedings of the Conference on Research and Development inInformation Retrieval (SIGIR), ACM, 2001.
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T. Joachims, N.Cristianini, and J. Shawe-Taylor, Composite Kernels for Hypertext Categorisation,Proceedings of the International Conference on Machine Learning (ICML), 2001.
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T. Joachims, The Webas the Bias. Poster at the Learning Workshop in Snowbird, 2001.
K. Morik, T. Joachims, M.Imhoff, P. Brockhausen, and S. Rueping, Integrating Kernel Methods into aKnowledge-Based Approach to Evidence-Based Medicine. In: L. Jain,Computational Intelligence Techniques in Medical Diagnosis and Prognosis,2001.
2000
T. Joachims, Estimatingthe Generalization Performance of a SVM Efficiently. Proceedings of theInternational Conference on Machine Learning (ICML), Morgan Kaufman, 2000.
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R. Klinkenberg and T.Joachims, Detecting Concept Drift with Support Vector Machines.Proceedings of the International Conference on Machine Learning (ICML),Morgan Kaufmann, 2000.
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K. Morik, M. Imhoff, P.Brockhausen, T. Joachims, and U. Gather, Knowledge Discovery and KnowledgeValidation in Intensive Care. Artificial Intelligence in Medicine, 2001.
[Elsevier][BibTeX]
1999
T. Joachims, MakingLarge-Scale SVM Learning Practical. In: Advances in Kernel Methods -Support Vector Learning, B. Schölkopf, C. Burges, and A. Smola (ed.), MITPress, 1999.
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T. Joachims, Wissenserlangung aus grossen Datenbanken. 9th Int.Symposium on Intensive Care, W.Kuckelt and K.Hankeln (ed.), Journal f.Anaesthesie und Intensivbehandlung, Pabst Science Publishers, 1999.
T. Joachims, TransductiveInference for Text Classification using Support Vector Machines.Proceedings of the International Conference on Machine Learning (ICML), 1999.
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T. Joachims, Aktuelles Schlagwort: Support Vector Machines.Künstliche Intelligenz, Vol. 4, 1999.
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T. Joachims, Estimatingthe Generalization Performance of a SVM Efficiently. LS8-Report25, Universität Dortmund, LS VIII, 1999.
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K. Morik, P. Brockhausen,and T. Joachims, Combining statistical learning with a knowledge-basedapproach - A case study in intensive care monitoring. Proceedings of theInternational Conference on Machine Learning (ICML), 1999.
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Tobias Scheffer andThorsten Joachims, Expected Error Analysis for Model Selection.Proceedings of the International Conference on Machine Learning (ICML), 1999.
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1998
Armstrong, Robert andFreitag, Dayne and Joachims, Thorsten and Mitchell, Tom, WebWatcher: ALearning Apprentice for the World Wide Web. Machine Learning and Data Mining,R. Michalski and I. Bratko and M. Kubat (ed.), Wiley, 1998, The file is acopy of Armstrong/etal/95a. Armstrong/etal/98a is a reprint of the 95adocument.
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T. Joachims and D.Mladenic, Browsing-Assistenten, Tour Guides und adaptive WWW-Server. KünstlicheIntelligenz, Vol. 3 (28), 1998.
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T. Joachims, TextCategorization with Support Vector Machines: Learning with Many RelevantFeatures. Proceedings of the European Conference on Machine Learning(ECML), Springer, 1998.
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Thorsten Joachims, Makinglarge-Scale SVM Learning Practical. LS8-Report 24, UniversitätDortmund, LS VIII-Report, 1998.
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Tobias Scheffer andThorsten Joachims, Estimating the expected error of empirical minimizersfor model selection. TR-98-9,TU-Berlin, 1998.
[BibTeX]
1997
Joachims, Thorsten andFreitag, Dayne and Mitchell, Tom, WebWatcher: A Tour Guide for the WorldWide Web. Proceedings of International Joint Conference on ArtificialIntelligence (IJCAI), Morgan Kaufmann, 1997.
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Joachims, Thorsten, AProbabilistic Analysis of the Rocchio Algorithm with TFIDF for TextCategorization. Proceedings of International Conference on MachineLearning (ICML), 1997.
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T. Joachims, TextCategorization with Support Vector Machines: Learning with Many RelevantFeatures. LS8-Report 23, Universität Dortmund, LS VIII-Report,1997.
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1996
J. Boyan and D. Freitagand T. Joachims, A Machine Learning Architecture for Optimizing Web SearchEngines. Proceedings of the AAAI Workshop on Internet Based InformationSystems, 1996.
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Joachims, Thorsten, Einsatz eines intelligenten, lernenden Agenten fürdas World Wide Web. Fachbereich Informatik, Universität Dortmund,Diplomarbeit, 1996.
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1995
Armstrong, Robert andFreitag, Dayne and Joachims, Thorsten and Mitchell, Tom, WebWatcher: ALearning Apprentice for the World Wide Web. Proceedings of the 1995 AAAISpring Symposium on Information Gathering from Heterogeneous, DistributedEnvironments, 1995.
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Joachims, Thorsten andMitchell, Tom and Freitag, Dayne and Armstrong, Robert, WebWatcher:Machine Learning and Hypertext. Beiträge zum 7. FachgruppentreffenMASCHINELLES LERNEN der GI-Fachgruppe 1.1.3, 1995, Forschungsbericht Nr. 580der Universität Dortmund.
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