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Mark Craven
2010 – today
- 2012
[j14]Andreas Vlachos, Mark Craven: Biomedical event extraction from abstracts and full papers using search-based structured prediction. BMC Bioinformatics 13(S-11): S5 (2012)- [i1]Keith Noto, Mark Craven: Learning Hidden Markov Models for Regression using Path Aggregation. CoRR abs/1206.3275 (2012)
- 2011
- [c33]David Andrzejewski, Xiaojin Zhu, Mark Craven, Benjamin Recht: A Framework for Incorporating General Domain Knowledge into Latent Dirichlet Allocation Using First-Order Logic. IJCAI 2011: 1171-1177
2000 – 2009
- 2009
- [j13]Adam A. Smith, Aaron L. Vollrath, Christopher A. Bradfield, Mark Craven: Clustered alignments of gene-expression time series data. Bioinformatics 25(12) (2009)
- [j12]Aaron L. Vollrath, Adam A. Smith, Mark Craven, Christopher A. Bradfield: EDGE3: A web-based solution for management and analysis of Agilent two color microarray experiments. BMC Bioinformatics 10: 280 (2009)
- [c32]David Andrzejewski, Xiaojin Zhu, Mark Craven: Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. ICML 2009: 4
- 2008
- [j11]Adam A. Smith, Aaron L. Vollrath, Christopher A. Bradfield, Mark Craven: Similarity Queries for Temporal Toxicogenomic Expression Profiles. PLoS Computational Biology 4(7) (2008)
- [c31]Burr Settles, Mark Craven: An Analysis of Active Learning Strategies for Sequence Labeling Tasks. EMNLP 2008: 1070-1079
- [c30]
- [c29]Keith Noto, Mark Craven: Learning Hidden Markov Models for Regression using Path Aggregation. UAI 2008: 444-451
- [e2]Ana L. C. Bazzan, Mark Craven, Natália F. Martins (Eds.): Advances in Bioinformatics and Computational Biology, Third Brazilian Symposium on Bioinformatics, BSB 2008, Santo André, Brazil, August 28-30, 2008. Proceedings. Lecture Notes in Computer Science 5167, Springer 2008, ISBN 978-3-540-85556-9
- 2007
- [j10]Keith Noto, Mark Craven: Learning probabilistic models of cis-regulatory modules that represent logical and spatial aspects. Bioinformatics 23(2): 156-162 (2007)
- [c28]Yue Pan, Tim Durfee, Joseph Bockhorst, Mark Craven: Connecting quantitative regulatory-network models to the genome. ISMB/ECCB (Supplement of Bioinformatics) 2007: 367-376
- [c27]
- 2006
- [j9]Keith Noto, Mark Craven: A specialized learner for inferring structured cis-regulatory modules. BMC Bioinformatics 7: 528 (2006)
- [c26]Andrew B. Goldberg, David Andrzejewski, Jurgen Van Gael, Burr Settles, Xiaojin Zhu, Mark Craven: Ranking Biomedical Passages for Relevance and Diversity: University of Wisconsin, Madison at TREC Genomics 2006. TREC 2006
- [e1]Tina Eliassi-Rad, Lyle H. Ungar, Mark Craven, Dimitrios Gunopulos (Eds.): Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Philadelphia, PA, USA, August 20-23, 2006. ACM 2006, ISBN 1-59593-339-5
- 2005
- [j8]Soumya Ray, Mark Craven: Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text. BMC Bioinformatics 6(S-1) (2005)
- [c25]Soumya Ray, Mark Craven: Supervised versus multiple instance learning: an empirical comparison. ICML 2005: 697-704
- [c24]Thomas Brow, Burr Settles, Mark Craven: Classifying Biomedical Articles by Making Localized Decisions. TREC 2005
- 2004
- [c23]Aaron E. Darling, Bob Mau, Mark Craven, Nicole T. Perna: Multiple Alignment of Rearranged Genomes. CSB 2004: 738-739
- [c22]Joseph Bockhorst, Mark Craven: Markov Networks for Detecting Overalpping Elements in Sequence Data. NIPS 2004
- [c21]Keith Noto, Mark Craven: Learning Regulatory Network Models that Represent Regulator States and Roles. Regulatory Genomics 2004: 52-64
- [c20]Burr Settles, Mark Craven: Exploiting Zone Information, Syntactic Rules, and Informative Terms in Gene Ontology Annotation of Biomedical Documents. TREC 2004
- 2003
- [j7]Joseph Bockhorst, Mark Craven, David Page, Jude W. Shavlik, Jeremy D. Glasner: A Bayesian Network Approach to Operon Prediction. Bioinformatics 19(10): 1227-1235 (2003)
- [j6]David Page, Mark Craven: Biological applications of multi-relational data mining. SIGKDD Explorations 5(1): 69-79 (2003)
- [c19]Marios Skounakis, Mark Craven, Soumya Ray: Hierarchical Hidden Markov Models for Information Extraction. IJCAI 2003: 427-433
- [c18]Joseph Bockhorst, Yu Qiu, Jeremy D. Glasner, Mingzhu Liu, Frederick R. Blattner, Mark Craven: Predicting bacterial transcription units using sequence and expression data. ISMB (Supplement of Bioinformatics) 2003: 34-43
- [c17]Marios Skounakis, Mark Craven: Evidence combination in biomedical natural-language processing. BIOKDD 2003: 25-32
- 2002
- [j5]Mark Craven: The Genomics of a Signaling Pathway: A KDD Cup Challenge Task. SIGKDD Explorations 4(2): 97-98 (2002)
- [c16]Joseph Bockhorst, Mark Craven: Exploiting Relations Among Concepts to Acquire Weakly Labeled Training Data. ICML 2002: 43-50
- 2001
- [j4]Mark Craven, Seán Slattery: Relational Learning with Statistical Predicate Invention: Better Models for Hypertext. Machine Learning 43(1/2): 97-119 (2001)
- [c15]Soumya Ray, Mark Craven: Representing Sentence Structure in Hidden Markov Models for Information Extraction. IJCAI 2001: 1273-1279
- [c14]Joseph Bockhorst, Mark Craven: Refining the Structure of a Stochastic Context-Free Grammar. IJCAI 2001: 1315-1322
- 2000
- [j3]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. Artif. Intell. 118(1-2): 69-113 (2000)
- [c13]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. ICML 2000: 199-206
- [c12]Mark Craven, David Page, Jude W. Shavlik, Joseph Bockhorst, Jeremy D. Glasner: A Probabilistic Learning Approach to Whole-Genome Operon Prediction. ISMB 2000: 116-127
1990 – 1999
- 1999
- [c11]Mark Craven, Johan Kumlien: Constructing Biological Knowledge Bases by Extracting Information from Text Sources. ISMB 1999: 77-86
- 1998
- [c10]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. AAAI/IAAI 1998: 509-516
- [c9]
- [c8]Seán Slattery, Mark Craven: Combining Statistical and Relational Methods for Learning in Hypertext Domains. ILP 1998: 38-52
- 1997
- [j2]Mark Craven, Jude W. Shavlik: Understanding Time-Series Networks: A Case Study in Rule Extraction. Int. J. Neural Syst. 8(4): 373-384 (1997)
- 1995
- [c7]Mark Craven, Richard J. Mural, Loren J. Hauser, Edward C. Uberbacher: Predicting Protein Folding Classes without Overly Relying on Homology. ISMB 1995: 98-106
- [c6]Mark Craven, Jude W. Shavlik: Extracting Tree-Structured Representations of Trained Networks. NIPS 1995: 24-30
- [c5]
- 1994
- [j1]Mark Craven, Jude W. Shavlik: Machine Learning Approaches to Gene Recognition. IEEE Expert 9(2): 2-10 (1994)
- [c4]Mark Craven, Jude W. Shavlik: Using Sampling and Queries to Extract Rules from Trained Neural Networks. ICML 1994: 37-45
- 1993
- [c3]Mark Craven, Jude W. Shavlik: Learning Symbolic Rules Using Artificial Neural Networks. ICML 1993: 73-80
- [c2]Mark Craven, Jude W. Shavlik: Learning to Represent Codons: A Challenge Problem for Constructive Induction. IJCAI 1993: 1319-1324
- 1991
- [c1]Geoffrey G. Towell, Mark Craven, Jude W. Shavlik: Constructive Induction in Knowledge-Based Neural Networks. ML 1991: 213-217
Coauthor Index
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last updated on 2013-01-06 20:19 CET by the dblp team