@inproceedings{conf/mva/KanungoHBSM94,
	title = {Document Degradation Models: Parameter Estimation and Model Validation.},
	author = {Tapas Kanungo and Robert M. Haralick and Henry S. Baird and Werner Stuetzle and David Madigan},
	booktitle = {MVA},
	crossref = {conf/mva/1994},
	pages = {552-557},
	url = {http://dblp.uni-trier.de/db/conf/mva/mva1994.html#KanungoHBSM94},
	year = {1994},
	biburl = {http://www.bibsonomy.org/bibtex/28f35b3cc25bdd76659418af622368217/dblp},
	description = {dblp},
	ee = {http://b2.cvl.iis.u-tokyo.ac.jp/mva/proceedings/CommemorativeDVD/1994/papers/1994552.pdf}, date = {2008-07-03},
	keywords = {dblp }
}

@article{journals/sigcse/StoneM07,
	title = {Integrating reflective writing in CS/IS.},
	author = {Jeffrey A. Stone and Elinor M. Madigan},
	journal = {SIGCSE Bulletin},
	number = {2},
	pages = {42-45},
	url = {http://dblp.uni-trier.de/db/journals/sigcse/sigcse39.html#StoneM07},
	volume = {39},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/26004367270b9acd95e4e8d159a46ecc6/dblp},
	description = {dblp},
	ee = {http://doi.acm.org/10.1145/1272848.1272881}, date = {2008-06-27},
	keywords = {dblp }
}

@article{journals/sigcse/StoneM08,
	title = {The impact of providing project choices in CS1.},
	author = {Jeffrey A. Stone and Elinor M. Madigan},
	journal = {SIGCSE Bulletin},
	number = {2},
	pages = {65-68},
	url = {http://dblp.uni-trier.de/db/journals/sigcse/sigcse40.html#StoneM08},
	volume = {40},
	year = {2008},
	biburl = {http://www.bibsonomy.org/bibtex/267f4172b4c81000e6e72774141d185ed/dblp},
	description = {dblp},
	ee = {http://doi.acm.org/10.1145/1383602.1383637}, date = {2008-06-27},
	keywords = {dblp }
}

@inproceedings{conf/vlsi/CodyMMH07,
	title = {High speed SOC design for blowfish cryptographic algorithm.},
	author = {Brian Cody and Justin Madigan and Spencer MacDonald and Kenneth W. Hsu},
	booktitle = {VLSI-SoC},
	crossref = {conf/vlsi/2007soc},
	pages = {284-287},
	publisher = {IEEE},
	url = {http://dblp.uni-trier.de/db/conf/vlsi/vlsisoc2007.html#CodyMMH07},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2ba60c56fe87015f428a935a84ffe14c5/dblp},
	description = {dblp},
	ee = {http://dx.doi.org/10.1109/VLSISOC.2007.4402513}, date = {2008-05-09},
	keywords = {dblp }
}

@article{Andersson1995,
	title = {On the relation between conditional independence models determined
	by finite distributive lattices and by directed acyclic graphs},
	author = {Steen A. Andersson and David Madigan and Michael D. Perlman and Christopher M. Triggs},
	day = {01},
	journal = {Journal of Statistical Planning and Inference},
	month = {Nov},
	number = {1},
	pages = {25--46},
	url = {http://www.sciencedirect.com/science/article/B6V0M-3YVCYY5-F/1/c3d74a00579f8f1e7642a4d09de43f0f},
	volume = {48},
	year = {1995},
	biburl = {http://www.bibsonomy.org/bibtex/2b8f77d34b8d6306187bbefba25214356/smicha},
	keywords = {62H99 }
}

@article{Hoeting1996,
	title = {A method for simultaneous variable selection and outlier identification in linear regression},
	author = {Jennifer Hoeting and Adrian E. Raftery and David Madigan},
	day = {15},
	journal = {Computational Statistics \& Data Analysis},
	month = {Jul},
	number = {3},
	pages = {251--270},
	url = {http://www.sciencedirect.com/science/article/B6V8V-3VW8N6H-3/1/fdc85cbaeb602f3aeea0c4fe33dd4219},
	volume = {22},
	year = {1996},
	biburl = {http://www.bibsonomy.org/bibtex/29a0982b1cc1adbedd540e1988306b6cd/smicha},
	keywords = {Bayesian averaging model }
}

@inproceedings{dorre99tm,
	title = {Text mining: finding nuggets in mountains of textual data},
	author = { {\noopsort{DZZ}}{J. Dörre and P. Gerstl and R. Seiffert}},
	booktitle = {Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining},
	editor = {Usama Fayyad and Surajit Chaudhuri and David Madigan},
	pages = {398-401},
	publisher = {ACM Press},
	url = {http://doi.acm.org/10.1145/312129.312299},
	year = {1999},
	biburl = {http://www.bibsonomy.org/bibtex/23872e75b78e758c74a46e8d2a0531e38/msn},
	location = {San Diego, California, United States}, isbn_ = {1-58113-143-7}, language = {english}, doi = {http://doi.acm.org/10.1145/312129.312299},
	keywords = {cites.gradu research.mining.text }
}

@inproceedings{conf/icdm/BalakrishnanM07,
	title = {Finding Predictive Runs with LAPS.},
	author = {Suhrid Balakrishnan and David Madigan},
	booktitle = {ICDM},
	crossref = {conf/icdm/2007},
	pages = {415-420},
	publisher = {IEEE Computer Society},
	url = {http://dblp.uni-trier.de/db/conf/icdm/icdm2007.html#BalakrishnanM07},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/215a66f68261161250452f2f9876506d0/dblp},
	description = {dblp},
	ee = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2007.84}, date = {2008-03-12},
	keywords = {dblp }
}

@inproceedings{Moskewicz01,
	title = {Chaff: Engineering an Efficient SAT Solver.},
	author = {Matthew W. Moskewicz and Conor F. Madigan and Ying Zhao and Lintao Zhang and Sharad Malik},
	booktitle = {DAC},
	crossref = {dac2001},
	pages = {530--535},
	publisher = {ACM},
	url = {http://dblp.uni-trier.de/db/conf/dac/dac2001.html\#MoskewiczMZZM01},
	year = {2001},
	biburl = {http://www.bibsonomy.org/bibtex/2db9023680efb151d536579b6b310d31d/marciomr},
	description = {dblp},
	date = {2002-12-16}, ee = {http://jamaica.ee.pitt.edu/Archives/ProceedingArchives/Dac/Dac2001/papers/2001/dac01/pdffiles/33_1.pdf}, isbn = {1-58113-297-2},
	keywords = {Chaff SAT impresso solver }
}

@article{Genkin:August2007:0040-1706:291,
	title = {Large-Scale Bayesian Logistic Regression for Text Categorization},
	author = {Alexander Genkin and David D. Lewis and David Madigan},
	journal = {Technometrics},
	pages = {291-304(14)},
	url = {http://www.ingentaconnect.com/content/asa/tech/2007/00000049/00000003/art00007},
	volume = {49},
	year = {August 2007},
	biburl = {http://www.bibsonomy.org/bibtex/211830035a44e5db49d194a7f7a3f35ed/jhammerb},
	description = {IngentaConnect Large-Scale Bayesian Logistic Regression for Text Categorization},
	abstract = {Logistic regression analysis of high-dimensional data, such as natural language text, poses computational and statistical challenges. Maximum likelihood estimation often fails in these applications. We present a simple Bayesian logistic regression approach that uses a Laplace prior to avoid overfitting and produces sparse predictive models for text data. We apply this approach to a range of document classification problems and show that it produces compact predictive models at least as effective as those produced by support vector machine classifiers or ridge logistic regression combined with feature selection. We describe our model fitting algorithm, our open source implementations (BBR and BMR), and experimental results.},
	doi = {doi:10.1198/004017007000000245},
	keywords = {classification data_mining logistic regression text_mining }
}