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Mark Craven :
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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 ] 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 ] 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 ] Mark Craven , Seán Slattery , Kamal Nigam First-Order Learning for Web Mining. [Citation Graph (0, 0)][DBLP ] ECML, 1998, pp:250-255 [Conf ] 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 ] 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 ] Mark Craven , Jude W. Shavlik Learning Symbolic Rules Using Artificial Neural Networks. [Citation Graph (0, 0)][DBLP ] ICML, 1993, pp:73-80 [Conf ] 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 ] Soumya Ray , Mark Craven Supervised versus multiple instance learning: an empirical comparison. [Citation Graph (0, 0)][DBLP ] ICML, 2005, pp:697-704 [Conf ] Joseph Bockhorst , Mark Craven Refining the Structure of a Stochastic Context-Free Grammar. [Citation Graph (0, 0)][DBLP ] IJCAI, 2001, pp:1315-1322 [Conf ] 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 ] 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 ] Marios Skounakis , Mark Craven , Soumya Ray Hierarchical Hidden Markov Models for Information Extraction. [Citation Graph (0, 0)][DBLP ] IJCAI, 2003, pp:427-433 [Conf ] 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 ] 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 ] 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 ] 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 ] 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 ] Marios Skounakis , Mark Craven Evidence combination in biomedical natural-language processing. [Citation Graph (0, 0)][DBLP ] BIOKDD, 2003, pp:25-32 [Conf ] Joseph Bockhorst , Mark Craven Markov Networks for Detecting Overalpping Elements in Sequence Data. [Citation Graph (0, 0)][DBLP ] NIPS, 2004, pp:- [Conf ] Mark Craven , Jude W. Shavlik Extracting Tree-Structured Representations of Trained Networks. [Citation Graph (0, 0)][DBLP ] NIPS, 1995, pp:24-30 [Conf ] Jeffrey C. Jackson , Mark Craven Learning Sparse Perceptrons. [Citation Graph (0, 0)][DBLP ] NIPS, 1995, pp:654-660 [Conf ] 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 ] 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 ] 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 ] 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 ] 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 ] 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 ] 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 ] 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 ] 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 ] Incorporating domain knowledge into topic modeling via Dirichlet Forest priors. [Citation Graph (, )][DBLP ] Learning Expressive Models of Gene Regulation. [Citation Graph (, )][DBLP ] Connecting quantitative regulatory-network models to the genome. [Citation Graph (, )][DBLP ] Multiple-Instance Active Learning. [Citation Graph (, )][DBLP ] Ranking Biomedical Passages for Relevance and Diversity: University of Wisconsin, Madison at TREC Genomics 2006. [Citation Graph (, )][DBLP ] Classifying Biomedical Articles by Making Localized Decisions. [Citation Graph (, )][DBLP ] Exploiting Zone Information, Syntactic Rules, and Informative Terms in Gene Ontology Annotation of Biomedical Documents. [Citation Graph (, )][DBLP ] Learning Hidden Markov Models for Regression using Path Aggregation. [Citation Graph (, )][DBLP ] An Analysis of Active Learning Strategies for Sequence Labeling Tasks. [Citation Graph (, )][DBLP ] Clustered alignments of gene-expression time series data. [Citation Graph (, )][DBLP ] Learning Statistical Models for Annotating Proteins with Function Information using Biomedical Text. [Citation Graph (, )][DBLP ] A specialized learner for inferring structured cis-regulatory modules. [Citation Graph (, )][DBLP ] EDGE3 : A web-based solution for management and analysis of Agilent two color microarray experiments. [Citation Graph (, )][DBLP ] Search in 0.003secs, Finished in 0.006secs