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Thorsten Joachims: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Thorsten Joachims, Dayne Freitag, Tom M. Mitchell
    Web Watcher: A Tour Guide for the World Wide Web. [Citation Graph (3, 0)][DBLP]
    IJCAI (1), 1997, pp:770-777 [Conf]
  2. Phoebe Sengers, Rainer Liesendahi, Werner Magar, Christoph Seibert, Boris Müller, Thorsten Joachims, Weidong Geng, Pia Mårtensson, Kristina Höök
    The enigmatics of affect. [Citation Graph (0, 0)][DBLP]
    Symposium on Designing Interactive Systems, 2002, pp:87-98 [Conf]
  3. Filip Radlinski, Thorsten Joachims
    Minimally Invasive Randomization fro Collecting Unbiased Preferences from Clickthrough Logs. [Citation Graph (0, 0)][DBLP]
    AAAI, 2006, pp:- [Conf]
  4. Tobias Scheffer, Thorsten Joachims
    Estimating the Expected Error of Empirical Minimizers for Model Selection. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1998, pp:1200- [Conf]
  5. Thorsten Joachims
    Text Categorization with Suport Vector Machines: Learning with Many Relevant Features. [Citation Graph (0, 0)][DBLP]
    ECML, 1998, pp:137-142 [Conf]
  6. Thomas Finley, Thorsten Joachims
    Supervised clustering with support vector machines. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:217-224 [Conf]
  7. Thorsten Joachims
    Estimating the Generalization Performance of an SVM Efficiently. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:431-438 [Conf]
  8. Thorsten Joachims
    Transductive Learning via Spectral Graph Partitioning. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:290-297 [Conf]
  9. Thorsten Joachims
    A support vector method for multivariate performance measures. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:377-384 [Conf]
  10. Thorsten Joachims
    A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:143-151 [Conf]
  11. Thorsten Joachims
    Transductive Inference for Text Classification using Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:200-209 [Conf]
  12. Thorsten Joachims, Nello Cristianini, John Shawe-Taylor
    Composite Kernels for Hypertext Categorisation. [Citation Graph (0, 0)][DBLP]
    ICML, 2001, pp:250-257 [Conf]
  13. Thorsten Joachims, John E. Hopcroft
    Error bounds for correlation clustering. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:385-392 [Conf]
  14. Ralf Klinkenberg, Thorsten Joachims
    Detecting Concept Drift with Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:487-494 [Conf]
  15. Katharina Morik, Peter Brockhausen, Thorsten Joachims
    Combining Statistical Learning with a Knowledge-Based Approach - A Case Study in Intensive Care Monitoring. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:268-277 [Conf]
  16. Tobias Scheffer, Thorsten Joachims
    Expected Error Analysis for Model Selection. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:361-370 [Conf]
  17. Ioannis Tsochantaridis, Thomas Hofmann, Thorsten Joachims, Yasemin Altun
    Support vector machine learning for interdependent and structured output spaces. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  18. Thorsten Joachims
    Optimizing search engines using clickthrough data. [Citation Graph (0, 0)][DBLP]
    KDD, 2002, pp:133-142 [Conf]
  19. Thorsten Joachims
    Training linear SVMs in linear time. [Citation Graph (0, 0)][DBLP]
    KDD, 2006, pp:217-226 [Conf]
  20. Filip Radlinski, Thorsten Joachims
    Query chains: learning to rank from implicit feedback. [Citation Graph (0, 0)][DBLP]
    KDD, 2005, pp:239-248 [Conf]
  21. Matthew Schultz, Thorsten Joachims
    Learning a Distance Metric from Relative Comparisons. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  22. Chun-Nam John Yu, Thorsten Joachims, Ron Elber, Jaroslaw Pillardy
    Support Vector Training of Protein Alignment Models. [Citation Graph (0, 0)][DBLP]
    RECOMB, 2007, pp:253-267 [Conf]
  23. Laura A. Granka, Thorsten Joachims, Geri Gay
    Eye-tracking analysis of user behavior in WWW search. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2004, pp:478-479 [Conf]
  24. Thorsten Joachims
    A Statistical Learning Model of Text Classification for Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2001, pp:128-136 [Conf]
  25. Thorsten Joachims, Laura A. Granka, Bing Pan, Helene Hembrooke, Geri Gay
    Accurately interpreting clickthrough data as implicit feedback. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2005, pp:154-161 [Conf]
  26. Thorsten Joachims
    Structured Output Prediction with Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    SSPR/SPR, 2006, pp:1-7 [Conf]
  27. Katharina Morik, Michael Imhoff, Peter Brockhausen, Thorsten Joachims, Ursula Gather
    Erratum to "Knowledge discovery and knowledge validation in intensive care". [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2000, v:20, n:2, pp:179- [Journal]
  28. Katharina Morik, Michael Imhoff, Peter Brockhausen, Thorsten Joachims, Ursula Gather
    Knowledge discovery and knowledge validation in intensive care. [Citation Graph (0, 0)][DBLP]
    Artificial Intelligence in Medicine, 2000, v:19, n:3, pp:225-249 [Journal]
  29. Paul Ginsparg, Paul Houle, Thorsten Joachims, Jae-Hoon Sul
    Mapping Subsets of Scholarly Information [Citation Graph (0, 0)][DBLP]
    CoRR, 2003, v:0, n:, pp:- [Journal]
  30. Lori Lorigo, Bing Pan, Helene Hembrooke, Thorsten Joachims, Laura A. Granka, Geri Gay
    The influence of task and gender on search and evaluation behavior using Google. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Manage., 2006, v:42, n:4, pp:1123-1131 [Journal]
  31. Thorsten Joachims, Fabrizio Sebastiani
    Guest Editors' Introduction to the Special Issue on Automated Text Categorization. [Citation Graph (0, 0)][DBLP]
    J. Intell. Inf. Syst., 2002, v:18, n:2-3, pp:103-105 [Journal]
  32. Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann, Yasemin Altun
    Large Margin Methods for Structured and Interdependent Output Variables. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2005, v:6, n:, pp:1453-1484 [Journal]
  33. Thorsten Joachims
    The Maximum-Margin Approach to Learning Text Classifiers. [Citation Graph (0, 0)][DBLP]
    KI, 2001, v:15, n:3, pp:63-65 [Journal]
  34. Thorsten Joachims
    Support Vector Machines (Aktuelles Schlagwort). [Citation Graph (0, 0)][DBLP]
    KI, 1999, v:13, n:4, pp:54-55 [Journal]
  35. Thorsten Joachims, Edda Leopold
    Text-Mining - Serviceteil. [Citation Graph (0, 0)][DBLP]
    KI, 2002, v:16, n:2, pp:39- [Journal]
  36. Thorsten Joachims, Dunja Mladenic
    Browsing-Assistenten, Tour Guides und adaptive WWW-Server. [Citation Graph (0, 0)][DBLP]
    KI, 1998, v:12, n:3, pp:23-29 [Journal]
  37. Susan T. Dumais, Thorsten Joachims, Krishna Bharat, Andreas S. Weigend
    SIGIR 2003 workshop report: implicit measures of user interests and preferences. [Citation Graph (0, 0)][DBLP]
    SIGIR Forum, 2003, v:37, n:2, pp:50-54 [Journal]
  38. Rich Caruana, Thorsten Joachims, Lars Backstrom
    KDD-Cup 2004: results and analysis. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2004, v:6, n:2, pp:95-108 [Journal]
  39. Stefan Pohl, Filip Radlinski, Thorsten Joachims
    Recommending related papers based on digital library access records. [Citation Graph (0, 0)][DBLP]
    JCDL, 2007, pp:417-418 [Conf]
  40. Filip Radlinski, Thorsten Joachims
    Active exploration for learning rankings from clickthrough data. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:570-579 [Conf]
  41. Benyah Shaparenko, Thorsten Joachims
    Information genealogy: uncovering the flow of ideas in non-hyperlinked document databases. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:619-628 [Conf]
  42. Yisong Yue, Thomas Finley, Filip Radlinski, Thorsten Joachims
    A support vector method for optimizing average precision. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2007, pp:271-278 [Conf]
  43. Carmel Domshlak, Thorsten Joachims
    Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation. [Citation Graph (0, 0)][DBLP]
    UAI, 2005, pp:169-177 [Conf]
  44. Stefan Pohl, Filip Radlinski, Thorsten Joachims
    Recommending Related Papers Based on Digital Library Access Records [Citation Graph (0, 0)][DBLP]
    CoRR, 2007, v:0, n:, pp:- [Journal]
  45. Filip Radlinski, Thorsten Joachims
    Query Chains: Learning to Rank from Implicit Feedback [Citation Graph (0, 0)][DBLP]
    CoRR, 2006, v:0, n:, pp:- [Journal]
  46. Filip Radlinski, Thorsten Joachims
    Evaluating the Robustness of Learning from Implicit Feedback [Citation Graph (0, 0)][DBLP]
    CoRR, 2006, v:0, n:, pp:- [Journal]
  47. Filip Radlinski, Thorsten Joachims
    Minimally Invasive Randomization for Collecting Unbiased Preferences from Clickthrough Logs [Citation Graph (0, 0)][DBLP]
    CoRR, 2006, v:0, n:, pp:- [Journal]
  48. Thorsten Joachims, Laura A. Granka, Bing Pan, Helene Hembrooke, Filip Radlinski, Geri Gay
    Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search. [Citation Graph (0, 0)][DBLP]
    ACM Trans. Inf. Syst., 2007, v:25, n:2, pp:- [Journal]
  49. Carmel Domshlak, Thorsten Joachims
    Efficient and non-parametric reasoning over user preferences. [Citation Graph (0, 0)][DBLP]
    User Model. User-Adapt. Interact., 2007, v:17, n:1-2, pp:41-69 [Journal]

  50. How does clickthrough data reflect retrieval quality? [Citation Graph (, )][DBLP]


  51. Learning structural SVMs with latent variables. [Citation Graph (, )][DBLP]


  52. Training structural SVMs when exact inference is intractable. [Citation Graph (, )][DBLP]


  53. Learning diverse rankings with multi-armed bandits. [Citation Graph (, )][DBLP]


  54. Predicting diverse subsets using structural SVMs. [Citation Graph (, )][DBLP]


  55. Interactively optimizing information retrieval systems as a dueling bandits problem. [Citation Graph (, )][DBLP]


  56. Training structural svms with kernels using sampled cuts. [Citation Graph (, )][DBLP]


  57. Identifying the Original Contribution of a Document via Language Modeling. [Citation Graph (, )][DBLP]


  58. Sparse Kernel SVMs via Cutting-Plane Training. [Citation Graph (, )][DBLP]


  59. Fast Active Exploration for Link-Based Preference Learning Using Gaussian Processes. [Citation Graph (, )][DBLP]


  60. Identifying the original contribution of a document via language modeling. [Citation Graph (, )][DBLP]


  61. Learning more powerful test statistics for click-based retrieval evaluation. [Citation Graph (, )][DBLP]


  62. Predicting structured objects with support vector machines. [Citation Graph (, )][DBLP]


  63. Search Engines that Learn from Implicit Feedback. [Citation Graph (, )][DBLP]


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