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Yan Liu 0002: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Yan Liu, Yiming Yang, Jaime G. Carbonell
    Boosting to correct inductive bias in text classification. [Citation Graph (0, 0)][DBLP]
    CIKM, 2002, pp:348-355 [Conf]
  2. Yan Liu, Jaime G. Carbonell, Rong Jin
    A New Pairwise Ensemble Approach for Text Classification. [Citation Graph (0, 0)][DBLP]
    ECML, 2003, pp:277-288 [Conf]
  3. John D. Lafferty, Xiaojin Zhu, Yan Liu
    Kernel conditional random fields: representation and clique selection. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  4. Yan Liu, Eric P. Xing, Jaime G. Carbonell
    Predicting protein folds with structural repeats using a chain graph model. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:513-520 [Conf]
  5. Jingrui He, Jaime G. Carbonell, Yan Liu
    Graph-Based Semi-Supervised Learning as a Generative Model. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:2492-2497 [Conf]
  6. Katharina Probst, Rayid Ghani, Marko Krema, Andrew E. Fano, Yan Liu
    Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:2838-2843 [Conf]
  7. Yan Liu, Jaime G. Carbonell, Vanathi Gopalakrishnan, Peter Weigele
    Protein Quaternary Fold Recognition Using Conditional Graphical Models. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:937-945 [Conf]
  8. Yan Liu, Jaime G. Carbonell, Peter Weigele, Vanathi Gopalakrishnan
    Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition. [Citation Graph (0, 0)][DBLP]
    RECOMB, 2005, pp:408-422 [Conf]
  9. Yan Liu, Jaime G. Carbonell, Judith Klein-Seetharaman, Vanathi Gopalakrishnan
    Context sensitive vocabulary and its application in protein secondary structure prediction. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2004, pp:538-539 [Conf]
  10. Yan Liu, Jaime G. Carbonell, Judith Klein-Seetharaman, Vanathi Gopalakrishnan
    Comparison of probabilistic combination methods for protein secondary structure prediction. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:17, pp:3099-3107 [Journal]
  11. Andrew Arnold, Yan Liu, Naoki Abe
    Temporal causal modeling with graphical granger methods. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:66-75 [Conf]
  12. Jun Yang, Yan Liu, Eric P. Xing, Alexander G. Hauptmann
    Harmonium Models for Semantic Video Representation and Classification. [Citation Graph (0, 0)][DBLP]
    SDM, 2007, pp:- [Conf]
  13. Katharina Probst, Rayid Ghani, Marko Krema, Andy Fano, Yan Liu
    Extracting and Using Attribute-Value Pairs from Product Descriptions on the Web. [Citation Graph (0, 0)][DBLP]
    WebMine, 2006, pp:41-60 [Conf]

  14. Graph-based transfer learning. [Citation Graph (, )][DBLP]

  15. Graph-Based Rare Category Detection. [Citation Graph (, )][DBLP]

  16. Undirected Graphical Models for Video Analysis and Classification. [Citation Graph (, )][DBLP]

  17. Who is the expert? Analyzing gaze data to predict expertise level in collaborative applications. [Citation Graph (, )][DBLP]

  18. Topic-link LDA: joint models of topic and author community. [Citation Graph (, )][DBLP]

  19. Learning Temporal Causal Graphs for Relational Time-Series Analysis. [Citation Graph (, )][DBLP]

  20. Grouped graphical Granger modeling methods for temporal causal modeling. [Citation Graph (, )][DBLP]

  21. Looking for Great Ideas: Analyzing the Innovation Jam. [Citation Graph (, )][DBLP]

  22. Learning dynamic temporal graphs for oil-production equipment monitoring system. [Citation Graph (, )][DBLP]

  23. Spatial-temporal causal modeling for climate change attribution. [Citation Graph (, )][DBLP]

  24. Proximity-Based Anomaly Detection Using Sparse Structure Learning. [Citation Graph (, )][DBLP]

  25. Grouped graphical Granger modeling for gene expression regulatory networks discovery. [Citation Graph (, )][DBLP]

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