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Mark Craven: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, Seán Slattery
    Learning to Extract Symbolic Knowledge from the World Wide Web. [Citation Graph (1, 0)][DBLP]
    AAAI/IAAI, 1998, pp:509-516 [Conf]
  2. Geoffrey G. Towell, Mark Craven, Jude W. Shavlik
    Constructive Induction in Knowledge-Based Neural Networks. [Citation Graph (1, 0)][DBLP]
    ML, 1991, pp:213-217 [Conf]
  3. Aaron E. Darling, Bob Mau, Mark Craven, Nicole T. Perna
    Multiple Alignment of Rearranged Genomes. [Citation Graph (0, 0)][DBLP]
    CSB, 2004, pp:738-739 [Conf]
  4. Mark Craven, Seán Slattery, Kamal Nigam
    First-Order Learning for Web Mining. [Citation Graph (0, 0)][DBLP]
    ECML, 1998, pp:250-255 [Conf]
  5. Joseph Bockhorst, Mark Craven
    Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:43-50 [Conf]
  6. Mark Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner
    Using Multiple Levels of Learning and Diverse Evidence to Uncover Coordinately Controlled Genes. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:199-206 [Conf]
  7. Mark Craven, Jude W. Shavlik
    Learning Symbolic Rules Using Artificial Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICML, 1993, pp:73-80 [Conf]
  8. Mark Craven, Jude W. Shavlik
    Using Sampling and Queries to Extract Rules from Trained Neural Networks. [Citation Graph (0, 0)][DBLP]
    ICML, 1994, pp:37-45 [Conf]
  9. Soumya Ray, Mark Craven
    Supervised versus multiple instance learning: an empirical comparison. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:697-704 [Conf]
  10. Joseph Bockhorst, Mark Craven
    Refining the Structure of a Stochastic Context-Free Grammar. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2001, pp:1315-1322 [Conf]
  11. Mark Craven, Jude W. Shavlik
    Learning to Represent Codons: A Challenge Problem for Constructive Induction. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1993, pp:1319-1324 [Conf]
  12. Soumya Ray, Mark Craven
    Representing Sentence Structure in Hidden Markov Models for Information Extraction. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2001, pp:1273-1279 [Conf]
  13. Marios Skounakis, Mark Craven, Soumya Ray
    Hierarchical Hidden Markov Models for Information Extraction. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2003, pp:427-433 [Conf]
  14. Seán Slattery, Mark Craven
    Combining Statistical and Relational Methods for Learning in Hypertext Domains. [Citation Graph (0, 0)][DBLP]
    ILP, 1998, pp:38-52 [Conf]
  15. Mark Craven, Johan Kumlien
    Constructing Biological Knowledge Bases by Extracting Information from Text Sources. [Citation Graph (0, 0)][DBLP]
    ISMB, 1999, pp:77-86 [Conf]
  16. Mark Craven, Richard J. Mural, Loren J. Hauser, Edward C. Uberbacher
    Predicting Protein Folding Classes without Overly Relying on Homology. [Citation Graph (0, 0)][DBLP]
    ISMB, 1995, pp:98-106 [Conf]
  17. Mark Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner
    A Probabilistic Learning Approach to Whole-Genome Operon Prediction. [Citation Graph (0, 0)][DBLP]
    ISMB, 2000, pp:116-127 [Conf]
  18. Joseph Bockhorst, Yu Qiu, Jeremy D. Glasner, Mingzhu Liu, Frederick R. Blattner, Mark Craven
    Predicting bacterial transcription units using sequence and expression data. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2003, pp:34-43 [Conf]
  19. Marios Skounakis, Mark Craven
    Evidence combination in biomedical natural-language processing. [Citation Graph (0, 0)][DBLP]
    BIOKDD, 2003, pp:25-32 [Conf]
  20. Joseph Bockhorst, Mark Craven
    Markov Networks for Detecting Overalpping Elements in Sequence Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  21. Mark Craven, Jude W. Shavlik
    Extracting Tree-Structured Representations of Trained Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:24-30 [Conf]
  22. Jeffrey C. Jackson, Mark Craven
    Learning Sparse Perceptrons. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:654-660 [Conf]
  23. Keith Noto, Mark Craven
    Learning Regulatory Network Models that Represent Regulator States and Roles. [Citation Graph (0, 0)][DBLP]
    Regulatory Genomics, 2004, pp:52-64 [Conf]
  24. Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, Seán Slattery
    Learning to construct knowledge bases from the World Wide Web. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 2000, v:118, n:1-2, pp:69-113 [Journal]
  25. Joseph Bockhorst, Mark Craven, David Page, Jude W. Shavlik, Jeremy D. Glasner
    A Bayesian Network Approach to Operon Prediction. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2003, v:19, n:10, pp:1227-1235 [Journal]
  26. Keith Noto, Mark Craven
    Learning probabilistic models of cis-regulatory modules that represent logical and spatial aspects. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2007, v:23, n:2, pp:156-162 [Journal]
  27. Mark Craven, Jude W. Shavlik
    Machine Learning Approaches to Gene Recognition. [Citation Graph (0, 0)][DBLP]
    IEEE Expert, 1994, v:9, n:2, pp:2-10 [Journal]
  28. Mark Craven, Jude W. Shavlik
    Understanding Time-Series Networks: A Case Study in Rule Extraction. [Citation Graph (0, 0)][DBLP]
    Int. J. Neural Syst., 1997, v:8, n:4, pp:373-384 [Journal]
  29. Mark Craven, Seán Slattery
    Relational Learning with Statistical Predicate Invention: Better Models for Hypertext. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:43, n:1/2, pp:97-119 [Journal]
  30. Mark Craven
    The Genomics of a Signaling Pathway: A KDD Cup Challenge Task. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2002, v:4, n:2, pp:97-98 [Journal]
  31. David Page, Mark Craven
    Biological applications of multi-relational data mining. [Citation Graph (0, 0)][DBLP]
    SIGKDD Explorations, 2003, v:5, n:1, pp:69-79 [Journal]

  32. Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. [Citation Graph (, )][DBLP]


  33. Learning Expressive Models of Gene Regulation. [Citation Graph (, )][DBLP]


  34. Connecting quantitative regulatory-network models to the genome. [Citation Graph (, )][DBLP]


  35. Multiple-Instance Active Learning. [Citation Graph (, )][DBLP]


  36. Ranking Biomedical Passages for Relevance and Diversity: University of Wisconsin, Madison at TREC Genomics 2006. [Citation Graph (, )][DBLP]


  37. Classifying Biomedical Articles by Making Localized Decisions. [Citation Graph (, )][DBLP]


  38. Exploiting Zone Information, Syntactic Rules, and Informative Terms in Gene Ontology Annotation of Biomedical Documents. [Citation Graph (, )][DBLP]


  39. Learning Hidden Markov Models for Regression using Path Aggregation. [Citation Graph (, )][DBLP]


  40. An Analysis of Active Learning Strategies for Sequence Labeling Tasks. [Citation Graph (, )][DBLP]


  41. Clustered alignments of gene-expression time series data. [Citation Graph (, )][DBLP]


  42. Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text. [Citation Graph (, )][DBLP]


  43. A specialized learner for inferring structured cis-regulatory modules. [Citation Graph (, )][DBLP]


  44. EDGE3: A web-based solution for management and analysis of Agilent two color microarray experiments. [Citation Graph (, )][DBLP]


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