Publications

Comment on Martínez-Delgado et al. Using Absorption Models for Insulin and Carbohydrates and Deep Learning to Improve Glucose Level Predictions.: Josiah Z. R. Misplon, Varun Saini, Brianna P. Sloves, Sarah H. Meerts and David R. Musicant. Sensors 2021, 21, 5273.
Playing with Matches: Adopting Gale-Shapley for Managing Student Enrollments Beyond CS2.: Anna N. Rafferty, David Liben-Nowell, David R. Musicant, Emy Farley, Allie Lyman, Ann May. Association for Computing Machinery Special Interest Group in Computer Science Education Technical Symposium (ACM SIGCSE), Portland, OR, March 21, 2024.
Open-Ended Robotics Exploration Projects for Budding Researchers.: David Musicant, Abha Laddha, Tom Choi. Proceedings of the 2017 AAAI Conference on Artificial Intelligence.
Barriers to the Localness of Volunteered Geographic Information: Shilad W. Sen, Heather Ford, David R. Musicant, Mark Graham, Oliver S.B. Keyes, Brent Hecht. Proceedings of the 2015 SIGCHI Conference on Human Factors in Computing Systems, CHI 2015.
Engaging High School Students in Modeling and Simulation through Educational Media: David R. Musicant and S. Selcen Guzey. Association for Computing Machinery Special Interest Group in Computer Science Education Technical Symposium (ACM SIGCSE), Kansas City, MO, March 6, 2015.
Getting to the Source: Where Does Wikipedia Get Its Information From?: Heather Ford, David R. Musicant, Shilad Sen, Nathaniel Miller. WikiSym '13: Proceedings of the 9th International Symposium on Wikis and Open Collaboration, 2013.
Keeping Wiki Content Current via News Sources: Rachel Adams, Alex Kuntz, Morgan Marks, William Martin, David R. Musicant. IUI '13 Companion: Proceedings of the Companion Publication of the 2013 International Conference on Intelligent User Interfaces, 2013, pp. 51-52.
SIGCSE '12: Proceedings of the 43rd ACM Technical Symposium on Computer Science Education: Laurie Smith King, David R. Musicant, Tracy Camp, Paul Tymann, Brad Miller (Editors). February 29-March 3, 2012. Association for Computing Machinery, 2012.
Mentoring in Wikipedia: A Clash of Cultures: David R. Musicant, Yuqing Ren, James A. Johnson, John Riedl. WikiSym '11: Proceedings of the 7th International Symposium on Wikis and Open Collaboration, 2011, pp. 173-182.
WP:Clubhouse? An Exploration of Wikipedia's Gender Imbalance: Shyong (Tony) K. Lam, Anuradha Uduwage, Zhenhua Dong, Shilad Sen, David R. Musicant, Loren Terveen, John Riedl. WikiSym '11: Proceedings of the 7th International Symposium on Wikis and Open Collaboration, 2011, pp. 1-10.
SIGCSE '11: Proceedings of the 42nd ACM Technical Symposium on Computer Science Education: Thomas J. Cortina, Ellen L. Walker, Laruie Smith King, David R. Musicant, Lester McCann (Editors). March 9-12, 2011. Association for Computing Machinery, 2011.
SIGCSE '10: Proceedings of the 41st ACM Technical Symposium on Computer Science Education: Gary Lewandowski, Steven Wolfman, Thomas J. Cortina, Ellen L. Walker, David R. Musicant (Editors). March 10-13, 2010. Association for Computing Machinery, 2010.
Environmental Chemistry through Intelligent Atmospheric Data Analysis: Deborah S. Gross, Robert Atlas, Jeffrey Rzeszotarski, Emma Turetsky, Janara Christensen, Sami Benzaid, Jamie Olson, Thomas Smith, Leah Steinberg, Jon Sulman, Anna Ritz, Benjamin Anderson, Catherine Nelson, David R. Musicant, Lei Chen, David C. Snyder, James J. Schauer. Environmental Modeling and Software, 25:6, pp. 760-769, June 2010.
Supervised Learning by Training on Aggregate Outputs: David R. Musicant, Robert Atlas, Janara M. Christensen, Jamie F. Olson, Jeffrey M. Rzeszotarski, Emma R. D. Turetsky. Expanded version of below conference paper. Carleton College Computer Science Technical report 2009drm1.
Understanding Support Vector Machine Classifications: A Local Approach: David Barbella, Sami Benzaid, Janara Christensen, Bret Jackson, Victor Qin, David Musicant. Proceedings of The International Conference on Data Mining (DMIN '09), Editors: Robert Stahlbock, Sven F. Crone, and Stefan Lessmann. CSREA Press, 2009, pages 305-311.
Supervised Learning by Training on Aggregate Outputs: David R. Musicant, Janara M. Christensen, Jamie F. Olson. Proceedings of the Seventh IEEE International Conference on Data Mining, IEEE Press, 2007, pages 252-261. Copyright (C) 2007 IEEE.
Predicting User-Perceived Quality Ratings from Streaming Media Data: Amy Csizmar Dalal, David R. Musicant, Jamie Olson, Brandy McMenamy, Sami Benzaid, Ben Kazez, Erica Bolan. Proceedings of the 2007 IEEE International Conference on Communications (ICC-2007), IEEE Press, 2007, pp. 65-72.
A Data Mining Course for Computer Science: Primary Sources and Implementations: David R. Musicant. Proceedings of the 2006 ACM SIGCSE Conference, Houston TX, pages 538-542. Copyright ACM, 2006. This is the author's version of the work. It was posted here by permission of ACM for personal use. Not for redistribution. The definitive version is posted as cited above.
Adapting K-Medians to Generate Normalized Cluster Centers: Benjamin J. Anderson, Deborah S. Gross, David R. Musicant, Anna M. Ritz, Thomas G. Smith, Leah E. Steinberg. Proceedings of the Sixth SIAM International Conference on Data Mining, Joydeep Ghosh, Diane Lambert, David Skillcorn, Jaideep Srivastava, editors, Society for Industrial and Applied Mathematics, Bethesda, MD, 2006, pages 165-175.
User-Friendly Clustering for Atmospheric Data Analysis: Benjamin J. Anderson, David R. Musicant, Anna M. Ritz, Andrew Ault, Deborah S. Gross, Melanie Yuen, Markus Gaelli. Carleton College Computer Science Technical Report 2005a.
Learning from Aggregate Views: Bee-Chung Chen, Lei Chen, Raghu Ramakrishnan, D.R. Musicant. Proceedings of the 22nd International Conference on Data Engineering, Ling Liu, Andreas Reuter, Kyu-Young Whang, Jianjun Zhang, editors, Atlanta, GA, 2006, page 3.
Support Vector Machines Illuminated: David R. Musicant. Encyclopedia of Data Warehousing and Mining, July 2005, pages 1071-1076.
The EDAM Project: Mining Atmospheric Aerosol Datasets: R. Ramakrishnan, J. J. Schauer, L. Chen, Z. Huang, M. Shafer, D. S. Gross, David R. Musicant. International Journal of Intelligent Systems, July 2005 (Volume 20 Issue 7), pages 759-787.
A Model for a Liberal Arts Project-Based Capstone Experience: David R. Musicant and Jeff Ondich. Carleton College Computer Science Technical Report 2004a, September 2004.
Mass Spectrum Labeling: Theory and Practice: Z. Huang, L. Chen, J.-Y. Cai, D.S. Gross, David R. Musicant, R. Ramakrishnan, J. J. Schauer, S. J. Wright. Proceedings of the Fourth IEEE International Conference on Data Mining, IEEE Press, 2004, pages 122-129. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Active Set Support Vector Regression: David R. Musicant and Alexander Feinberg. IEEE Transactions on Neural Networks 15, March 2004, 268-275. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Optimizing F-Measure with Support Vector Machines: David R. Musicant, V. Kumar, and A. Ozgur. Proceedings of the Sixteenth International Florida Artificial Intelligence Research Society Conference, I. Russell and S. Haller, editors, AAAI Press 2003, 356-360.
Weka-Parallel: Machine Learning in Parallel: Sebastian Celis and David R. Musicant. Carleton College Computer Science Technical Report 2002b, August 2002.
Large Scale Kernel Regression via Linear Programming: O. L. Mangasarian and David R. Musicant. Machine Learning 46, January 2002, 255-269.
Optimization Methods in Massive Datasets: P. S. Bradley, O. L. Mangasarian and David R. Musicant. "Handbook of Massive Datasets", J. Abello , P. M. Pardalos, M. G. C. Resende, editors, Kluwer Academic Publishers 2002, 439-472.
NCV: A Machine Learning Environment for Parallel Evaluation: Brad Davis, Ethan Sommer, and David R. Musicant. Carleton College Computer Science Technical Report 2001-01, August 2001.
Lagrangian Support Vector Machines: O. L. Mangasarian and David R. Musicant. Journal of Machine Learning Research 1, March 2001, 161-177.
Active Set Support Vector Machine Classification: O. L. Mangasarian and David. R. Musicant. Advances in Neural Information Processing Systems 13, Todd K. Leen, Thomas G. Dietterich, and Volker Tresp, editors. MIT Press, Cambridge, MA, 2001, pages 577-583.
Data Discrimination via Nonlinear Generalized Support Vector Machines: O. L. Mangasarian and David R. Musicant Complementarity: Applications, Algorithms and Extensions, M. C. Ferris, O. L. Mangasarian and J.-S. Pang, editors, Kluwer Academic Publishers, Dordrecht, The Netherlands, 2001, pages 233-251.
Robust Linear and Support Vector Regression: O. L. Mangasarian and D. R. Musicant. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, September 2000, 950-955. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Data Mining via Mathematical Programming and Machine Learning: D. R. Musicant. Ph.D. Thesis, University of Wisconsin - Madison, July, 2000.
Successive Overrelaxation for Support Vector Machines: O. L. Mangasarian and David R. Musicant. IEEE Transactions on Neural Networks, 10, 1999, 1032-1037. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.