Michael J. Pazzani
Vice President for Research and Graduate and Professional Education
Rutgers, The State University of New Jersey
3 Rutgers Plaza, ASB III-3rd Floor
New Brunswick, NJ 08901-8559
pazzani@rutgers.edu
Phone: (732) 932-1500
Fax: (732)932-0145

PUBLICATIONS

|2011 | |2010 | |2007 | |2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 |
1999 | 1998 | 1997 | 1996 | 1995 | 1994 | 1993 | 1992 | 1991 | 1990 |
| 1989 | 1988 | 1987 | 1986 | 1985 | 1984 | 1983 |

2011

Chris Mesterharm, Michael J. Pazzani: Active learning using on-line algorithms. KDD 2011: 850-858 PDF

2010

Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, Michael J. Pazzani: An energy-efficient mobile recommender system. KDD 2010: 899-908 PDF

2007

Daniel Billsus, and Michael J. Pazzani (2007). Adaptive News Access , In "The Adaptive Web: Methods and Strategies of Web Personalization" (Springer, LNCS #4321), February 1, 2007 PDF

Michael J. Pazzani and Daniel Billsus, (2007).Content-based Recommendation Systems. In "The Adaptive Web: Methods and Strategies of Web Personalization" (Springer, LNCS #4321), February 1, 2007 PDF

2006

Rita Koch & Michael Pazzani. An Analysis of CISE Funding in FY 2005, Computing Research News, Vol. 17/No. 1 HTML

2005

Pazzani,M. (2005). Beyond Idiot Savants: Recommendations and Common Sense. Workshop on Beyond personalization: the next stage of recommender systems research. Intelligent User Interfaces, San Diego, CA PDF

Billus, D., Klefstad, R & Pazzani, M.(2005). Adaptive Personalization of Mobile Content Delivery. Human Computer Interaction, Las Vegas. PDF

Pazzani, M. & Brunk, C. (2005). An Improved Representation for Content-Based Television Personalization. Human Computer Interaction, Las Vegas. PDF

2004

Kamal Abdali, Gregory Andrews, Mari Maeda, Michael Pazzani (2004). NSF/CISE Plans for FY2005. Computing Research News, Vol. 16/No. 2 HTML Michael Pazzani, Kamal Abdali, Greg Andrews & Sangtae Kim (2004) CISE Update: Adjusting to the Increase in Proposals. Computing Research News, Vol. 16/No. 5 HTML

2003

Keogh, E., Chu, S., Hart, D., Pazzani, M. Segmenting Time Series: A Survey and Novel Approach. Data Mining in Time Series Databases. World Scientific Publishing Company PDF

2002

Daniel Billsus, Clifford A. Brunk, Craig Evans, Brian Gladish and Michael Pazzani (2002). Adaptive interfaces for ubiquitous web Communications of The ACM, May 2002, Vol 45, No 5. pg 34-38. PDF

Chu, S., Keogh, E., Hart, D., Pazzani, M. (2002). Iterative Deepening Dynamic Time Warping for Time Series. The Second SIAM International Conference on Data Mining (SDM-02), 2002. PDF

Pazzani, M. and Billsus, D. (2002). Adaptive Web Site Agents. Journal of Agents and Multiagent systems,5(2) 205-218 PDF

Keogh, E., , K., Mehrotra S. & Pazzani, M (2002). Locally adaptive dimensionality reduction for indexing large time series databases. ACM Transactions on Database Systems, 27(2) 188-228. PDF

Keogh, E. & Pazzani, M. (2002). Learning the Structure of Augmented Bayesian Classifiers. International Journal on Artificial Intelligence Tools. Vol 11. No 4, 587-601. PDF

Miyahara, K. and Pazzani, M. J. (2002). Improvement of Collaborative Filtering with the Simple Bayesian Classifier. IPSJ Journal, Vol.43, No.11, Information Processing Society of Japan, November, 2002 PDF

Pazzani, M. (2002). Commercial Applications of Machine Learning for personalized wireless portals. Pacific Rim Conference on Artificial Intelligence, Springer. Pp 1-5. PDF

2001

Bay, S. D. and Pazzani, M. J. (2001). Detecting Group Differences: Mining Contrast Sets. Data Mining and Knowledge Discovery. Vol 5, No 3 213-246. PDF.

Bay, S. D., Kibler, D., Pazzani, M. J., and Smyth, P. (2001). The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. In Information Processing Society of Japan Magazine. Volume 42, Number 5, pages 462-466. English language version reprinted in SIGKDD Explorations. Volume 2, Issue 2, pages 81-85, 2000. PDF.

Keogh, E., Chu, S., Hart, D. & Pazzani, M. (2001). An Online Algorithm for Segmenting Time Series. IEEE International Conference on Data Mining. PDF

Keogh, E., Chu, S., & Pazzani, M. (2001). Ensemble-Index: A New Approach to Indexing Large Databases. In 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco. PDF

Keogh, E., Chakrabarti, K., Pazzani, M., & Mehrotra (2001). Locally adaptive dimensionality reduction for indexing large time series databases. SIGMOD 2001. Best paper award. PDF

Keogh, E., S. Chu & Pazzani, M. (2001). Using ensembles of representations for indexing large databases. International Workshop on Mining Spatial and Temporal data. In conjunction with the Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-01). PDF

Keogh, E. & Pazzani, M. (2001). Derivative Dynamic Time Warping. In First SIAM International Conference on Data Mining (SDM'2001), Chicago, USA. PDF

M. J. Pazzani, S. Mani, W. R. Shankle (2001). Acceptance of Rules Generated by Machine Learning among Medical Experts. Methods of Information in Medicine; 40: 380-385. PDF

George Buchanan, Sarah Farrant, Matt Jones, Harold W. Thimbleby, Gary Marsden, Michael J. Pazzani: Improving mobile internet usability. WWW 2001: 673-680. HTML PDF

G. Webb, Michael J. Pazzani, Daniel Billsus, (2001).Machine learning for user modeling. User Modeling and User-Adapted Interaction 11: 19-20, 2001. PDF

2000

Bay, S. D. and Pazzani, M. J. (2000). Discovering and Describing Category Differences: What makes a discovered difference insightful?. In Proceedings of the Twenty Second Annual Meeting of the Cognitive Science Society. PDF.

Keogh, E., Chakrabarti, K., Pazzani, M. & Mehrotra, S (2000) Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems 3(3): 263-286. PDF

Bay, S. D. and Pazzani, M. J. (2000). Characterizing Model Errors and Differences. In Proceedings of the Seventeenth International Conference on Machine Learning. PDF.

Bay, S. D. and Pazzani, M. J. (2000). Characterizing Model Performance in the Feature Space. In ICML 2000 Workshop on What Works Well Where?. PDF.

Keogh, E. & Pazzani, M. (2000) Scaling up Dynamic Time Warping for Datamining Applications. In 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, 2000. PDF

Keogh, E. & Pazzani, M. (2000) A simple dimensionality reduction technique for fast similarity search in large time series databases.In the Fourth Pacific- Asia Conference on Knowledge Discovery and Data Mining. Kyoto, Japan. PDF.

Michael J. Pazzani: Representation of electronic mail filtering profiles: a user study. Intelligent User Interfaces 2000: 202-206 PDF

Daniel Billsus, Michael J. Pazzani, James Chen: A learning agent for wireless news access. Intelligent User Interfaces 2000: 33-36 PDF

Koji Miyahara, Michael J. Pazzani: Collaborative Filtering with the Simple Bayesian Classifier. PRICAI 2000: 679-689 PDF.

Pazzani, M. (2000). Learning with Globally Predictive Tests. New Generation Computing 18(1): 28-38 PDF Postscript

Pazzani, M. (2000). Knowledge discovery from data? IEEE Intelligent Systems 15(2): 10-13 (2000) PDF

Billsus, D., and Pazzani, M. (2000). "User Modeling for Adaptive News Access". User Modeling and User-Adapted Interaction. 10:2/3. 147-180 PDF

1999

Lathrop, R. & Pazzani, M. (1999). Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses. Journal of Combinatorial Optimization. 3, 301-320. PDF Postscript

Billsus, D. and Pazzani, M. (1999). "A Hybrid User Model for News Story Classification", Proceedings of the Seventh International Conference on User Modeling (UM '99), Banff, Canada. PDF Postscript

Billsus, D. and Pazzani, M. (1999). "A Personal News Agent that Talks, Learns and Explains", Proceedings of the Third International Conference on Autonomous Agents (Agents '99), Seattle, Washington. PDF Postscript

Bay, S. D. and Pazzani, M. J. (1999). Detecting Change in Categorical Data: Mining Contrast Sets. In Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining. PDF Postscript

Pazzani, M. J. and Bay, S. D. (1999). The Independent Sign Bias: Gaining Insight from Multiple Linear Regression. In Proceedings of the Twenty-First Annual Meeting of the Cognitive Science Society. PDF Postscript Overheads PDF

Pazzani, M. and Billsus, D. (1999). "Adaptive Web Site Agents". Proceedings of the Third International Conference on Autonomous Agents (Agents '99), Seattle, Washington. PDF Postscript

Keogh, E. & Pazzani, M. (1999). Relevance Feedback Retrieval of Time Series Data. The Twenty-Second Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval. PDF Postscript

Eamonn J. Keogh, Michael J. Pazzani: (1999). Scaling up Dynamic Time Warping to Massive Datasets. Principles and Practice of Knowledge Discovery in Databases, Prague, Czech Republic. PDF Postscript

Eamonn J. Keogh, Michael J. Pazzani: (1999). An indexing scheme for similarity search in large time series databases. The 11th International Conference on Scientific and Statistical Database Management. Cleveland, Ohio. PDF Postscript

S. Mani, M.B. Dick, M.J. Pazzani, E.L. Teng, D. Kempler, I.M. Taussig (1999). Refinement of Neuro-Psychological Tests for Dementia Screening in a Cross Cultural Population Using Machine Learning. Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making Aalborg, Denmark. PDF

Pazzani, M. & Billsus, D. (1999). Evaluating Adaptive Web site Agents. Workshop on Recommender Systems Algorithms and Evaluation, 22nd International Conference on Research and Development in Information Retrieval. PDF Postscript

Keogh, E. & Pazzani. M. (1999). Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. Uncertainty 99, 7th. Int'l Workshop on AI and Statistics, Ft. Lauderdale, Florida, 225-230. Postscript PDF

Merz, C. & Pazzani, M. (1999). A Principal Components Approach to Combining Regression Estimates Machine Learning. 36, 9-32. PDF Postscript

Pazzani, M. (1999). A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review. 13(5-6): 393-408 PDF

Mani, S., Shankle, R., Dick, M and Pazzani, M. (1999) Two-Stage Machine Learning Model for Guideline Development Artificial Intelligence in Medicine, 16, 51-80. PDF

Lathrop, R.H., Steffen, N.R., Raphael, M., Deeds-Rubin, S., Pazzani, M.J., Cimoch, P.J., See, D.M., Tilles, J.G.; (1999) Knowledge-based Avoidance of Drug-Resistant HIV Mutants. AI Magazine, volume 20, number 1, Spring 1999, pages 13-25. PDF Postscript

Ian Soboroff, Charles K. Nicholas, Michael J. Pazzani: Workshop on Recommender Systems: Algorithms and Evaluation. SIGIR Forum 33(1): 36-43 (1999) HTML

Subramani Mani, Malcolm B. Dick, Michael J. Pazzani, Evelyn L. Teng, Daniel Kempler, I. Maribell Taussig: Refinement of Neuro-psychological Tests for Dementia Screening in a Cross Cultural Population Using Machine Learning. Lecture Notes in Artificial Intelligence: Artificial Intelligence in Medicine, AIMDM'99, Vol. 1620, p326-335. PDF

1998

Keogh, E., & Pazzani, M. (1998). An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. Proceedings of the Fourth International Conference of Knowledge Discovery and Data Mining. pp 239-241, AAAI Press. Postscript PDF

Lathrop, R., Steffen, N., Raphael, M., Deeds-Rubin, S., Pazzani, M., Cimoch, P., See, D., Tilles, J. (1998) Knowledge-based Avoidance of Drug-Resistant HIV Mutants Proceedings of the 10th Conference on Innovatiove Applications of Artificial Intelligence, Madison, Wisc. Postscript PDF

Billsus, D. & Pazzani, M. (1998). Learning Collaborative Information Filters. Proceedings of the International Conference on Machine Learning. Morgan Kaufmann Publishers. Madison, Wisc. PDF

Webb, G. & Pazzani, M. (1998). Adjusted Probability Naive Bayesian Induction. 11th Australian Joint Conference on Artificial Intelligence. Brisbane, QLD. Australia PDF

Keogh, E. & Pazzani, M. (1998). An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. AAAI Workshop on Predicting the Future: AI Approaches to Time-Series Analysis. Madison, Wisc. PDF

Pazzani, M. (1998). Learning with Globally Predictive Tests. The First International Conference on Discovery Science Fukuoka, Japan PDF

Mani, S. and Pazzani, M. (1998). Guideline Generation from Data by Induction of Decision Tables Using a Bayesian Network Framework JAMIA supplement p518-522, 1998.PDF

Shankle, R., Mani, S., Dick, M and Pazzani, M. (1998) Simple Models for Estimating Dementia Severity Using Machine Learning MedInfo'98: 9th World Congress on Medical Informatics, Seoul, Korea, August 1998, proceedings. PDF

Cimoch, P.J., See, D.M., Pazzani, M.J., Reiter, W.M., Lathrop, R.H., Fasone, W.A, Tilles, J.G.; (1998). Application of a genotypic driven rule-based expert artificial intelligence computer system in treatment experienced HIV-infected patients. Immunologic and virologic response." Proc. of the 12th World AIDS Conf., Geneva, Switzerland, extended abstract #32297 Postscript. PDF.

1997

Pazzani, M., (1997) Comprehensible Knowledge Discovery: Gaining Insight from Data. First Federal Data Mining Conference and Exposition. pg 73-82. Washington, DC. PDF Overheads PDF

Pazzani, M., Iyer, R., See, D., Shroeder, E., & Tilles, J. (1997). CTSHIV: A Knowledge-based System in the Management of HIV-infected patients. Proceedings of the International Conference on Intelligent Information Systems PDF Postscript

Billsus, Daniel & Pazzani, M. (1997) Learning Probabilistic User Models. in Workshop Notes of "Machine Learning for User Modeling", Sixth International Conference on User Modeling, Chia Laguna, Sardinia. PDF Postscript

Merz, C., & Pazzani M. (1997). Combining Neural Network Regression Estimates Using Principal Components. "Preliminary Papers of the 6th International Workshop on Artificial Intelligence and Statistics". PDF Postscript

Domingos, P., & Pazzani, M. (1997). Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. Machine Learning. 29, 103-130. PDF. Postscript. PDF.

Pazzani, M., See, D., Shroeder, E., & Tilles, J. (1997). Application of an Expert System in the Management of HIV-infected patients. Journal of AIDS and Human Retrovirology. 15:356-362. PDF

Pazzani, M. (1997). Searching for dependencies in Bayesian classifiers. Artificial Intelligence and Statistics IV, Lecture Notes in Statistics, Springer-Verlag: New York. PDF Postscript Overheads HTML

Pazzani, M., Mani, S., & Shankle, W. R. (1997). Beyond concise and colorful: learning intelligible rules. Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA. AAAI Press, 235-238. PDF

Pazzani, M., Mani, S. & Shankle, W. R. (1997). Comprehensible knowledge-discovery in databases. In M. G. Shafto and P. Langley (Ed.), Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, pp. 596-601. Lawrence Erlbaum. PDF

Pazzani M., & Billsus, D. (1997). Learning and Revising User Profiles: The identification of interesting web sites. Machine Learning 27, 313-331. PDF

Merz, C., & Pazzani, M. (1997). Combining Neural Network Regression Estimates Using Principal Components. The Sixth International Workshop on Artificial Intelligence and Statistics. PDF Postscript

Mani, M., McDermott, S., & Pazzani, M. (1997) Generating Models of Mental Retardation from Data with Machine Learning Proceedings IEEE Knowledge and Data Engineering Exchange Workshop (KDEX-97), p114-119, IEEE Computer Society. PDF Postscript

Mani, M., McDermott, S., & Pazzani, M. (1997). Detecting Mental Retardation in Newborns and Infants: A Machine Learning Approach. Pediatrics Supplement Vol. 100, No. 3, part 2, p443 PDF Postscript

Merz, C., & Pazzani, M. (1997). Handling Redundancy in Ensembles of Learned Models Using Principal Components. AAAI Workshop on Integrating Multiple Models. PDF Postscript

M. Ackerman, D. Billsus, S. Gaffney, S. Hettich, G. Khoo, D. Kim, R. Klefstad, C. Lowe, A. Ludeman, J. Muramatsu, K. Omori, M. Pazzani , D. Semler, B. Starr, & P. Yap (1997). Learning Probabilistic User Profiles: Applications to Finding Interesting Web Sites, Notifying Users of Relevant Changes to Web Pages, and Locating Grant Opportunities. AI Magazine 18(2) 47-56. PDF

W.R. Shankle, Subramani Mani, Michael J. Pazzani, and Padhraic Smyth. (1997) ``Detecting very early stages of Dementia from normal aging with Machine Learning methods''. In Keravnou, E., Garbay, C., Baud, R., and Wyatt, editors, Lecture Notes in Artificial Intelligence: Artificial Intelligence in Medicine, AIME97, volume 1211, pages 73-85, Springer PDF Postscript

W.R. Shankle, Subramani Mani Michael J. Pazzani, and Padhraic Smyth. (1997) ``Dementia Screening with Machine Learning methods.'' In Intelligent Data Analysis in Medicine and Pharmacology, Eds. Elpida Keravnou, Nada Lavrac and Blaz Zupan, Kluwer Academic Publishers. PDF Postscript

Subramani Mani, W.R. Shankle, Michael J. Pazzani, Padhraic Smyth, and Malcolm B. Dick. (1997) ``Differential Diagnosis of Dementia: A Knowledge Discovery and Data Mining (KDD) Approach''. American Medical Informatics Association (AMIA) Annual Fall Symposium, Nashville, HTML PDF

Shankle, W.R., Mani, S., Pazzani, M. J. and Smyth, P. (1997). Use of a Computerized Patient Record Database of Normal Aging and Very Mildly Demented Subjects to Compare Classification Accuracies Obtained with Machine Learning Methods and Logistic Regression. Computing Science and Statistics, 29: 201-209.

1996

Pazzani, M., Muramatsu J., & Billsus, D. (1996). Syskill & Webert: Identifying interesting web sites. AAAI Spring Symposium. Stanford, CA. HTML PDF

Starr, B., Ackerman, M., & Pazzani, M. (1996). Do I Care? -- Tell Me What's Changed on the Web. AAAI Spring Symposium. Stanford, CA. PDF Postscript

Merz, C. J., Pazzani, M. J. (1996) Handling Redundancy in Ensembles of Learned Models Using Principal Components. Presented at the Workshop on Integrating Multiple Learned Models for Improving and Scaling Machine Learning Algorithms at AAAI-96. PDF Postscript

Shankle, R., Datta, P., & Pazzani, M. (1996). Applying machine learning to an Alzheimer's database, AAAI-96 Spring Symposium AI in Medicine: Applications of Current Technologies. Stanford, CA. PDF Postscript

Ali, K., & Pazzani M. (1996). Error Reduction through Learning Multiple Descriptions Machine Learning, 24:3. Postscript PDF

Merz, C., Pazzani, M., & Danyluk, A. (1996). Tuning Numeric Parameters to troubleshoot a telephone network. IEEE Expert, Feb. 1996, pg. 44-49. PDF

Shankle, W.R., Datta, P., Dillencourt, M., & Pazzani, M. (1996). Improving Dementia Screening Tests with Machine Learning Methods. Alzheimer's Research. PDF Postscript

Pazzani, M. (1996). Review of "Inductive Logic Programming". Machine Learning, 23, 103-108. PDF

Yamazaki, T., Pazzani, M., & Merz, C. (1996). Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique. In Wermter, Riloff & Scheler (Eds.) Connectionist, Statistical, and Symbolic Approaches to Learning for Natural Language Processing. PDF

Domingos, P., & Pazzani, M. (1996). Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. Proceedings of the International Conference on Machine Learning. PDF. Postscript.

Pazzani, M., Muramatsu J., & Billsus, D. (1996). Syskill & Webert: Identifying interesting web sites. Proceedings of the National Conference on Artificial Intelligence, Portland, OR. Slides HTML HTML Postscript PDF

Billsus, D., & Pazzani, M. (1996). Revising user profiles: The search for interesting Web sites. International Multi-Strategy Learning Conference. Harpers Ferry, VA. PDF Postscript

Pazzani, M. (1996). Constructive Induction of Cartesian Product Attributes. Information, Statistics and Induction in Science. Melbourne, Australia. PDF Postscript

Starr, B., Ackerman, M., & Pazzani, M. (1996). "Do-I-Care: A Collaborative Web Agent." Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI'96), April, 1996, pp. 273-274. PDF HTML

Merz, C., & Pazzani, M. (1996). Combining neural network regression estimates with regularized linear weights. Advances in Neural Information Processing Systems 9, Proceedings of the 1996 Conference. MIT Press, 564-570. PDF Postscript of Final 1997 Version

1995

Ali, K., Brunk, C., & Pazzani, M. (1995). Learning Multiple Relational Rule-based Models. In "Preliminary Papers of the 5th International Workshop on Artificial Intelligence and Statistics". Fort Lauderdale, FL. PDF Postscript

Pazzani, M. (1995). Searching for dependencies in Bayesian classifiers. In "Preliminary Papers of the 5th International Workshop on Artificial Intelligence and Statistics". Fort Lauderdale, FL. PDF Postscript

Hirschberg, D., Pazzani. M., & Ali, K. (1995). Average case analysis of k-CNF and k-DNF learning algorithms. In S. Hanson, M. Kearns, T. Petsche and R. Rivest Computational Learning Theory and Natural Learning Systems: Constraints and Prospects. Cambridge, MA: MIT Press. PDF Postscript

Ali, K., & Pazzani, M. (1995). HYDRA-MM: Learning Multiple Descriptions to Improve Classification Accuracy. International Journal on Artificial Intelligence Tools, 4. PDF Postscript

Yamazaki, T., Pazzani, M., & Merz, C. (1995). Learning Hierarchies from Ambiguous Natural Language Data, Proceedings of the 12th International Conference of Machine Learning. PDF

Brunk, C., & Pazzani, M. (1995). A Linguistically-Based Semantic Bias for Theory Revision Proceedings of the 12th International Conference of Machine Learning. PDF Postscript

Hume, T., & Pazzani, M. (1995). Learning Sets of Related Concepts: A Shared Task Model. Proceedings of the Sixteen Annual Conference of the Cognitive Science Society. Pittsburgh, PA: Lawrence Erlbaum. PDF HTML Slides HTML

Pazzani, M. (1995). An iterative-improvement approach for the discretization of numeric attributes in Bayesian classifiers. Proceedings of the First International Conference on Knowledge Discovery and Data Mining. Montreal: AAAI Press PDF

Pazzani, M., Nguyen, L., & Mantik, S. (1995). Learning from hotlists and coldlists: Towards a WWW information filtering and seeking agent. In Proceedings of the Seventh International Conference on Tools with Artificial Intelligence HTML. PDF.

1994

Pazzani, M. (1994). Learning causal patterns: Making a transition from data-driven to theory-driven learning. In Ryszard Michalski & Georghe Tecuci (Eds.) Machine Learning (Vol. IV): A Multi-Strategy Approach. San Mateo, CA: Morgan Kaufmann. PDF of 1993 Journal Version.

Murphy, P., & Pazzani, M. (1994). Revision of production system rule-bases. Proceedings of the 11th International Conference of Machine Learning, New Brunswick. Morgan Kaufmann, 199-200. PDF Postscript

Murphy, P. & Pazzani, m. (1994). Automated Revision of CLIPS Rule-Bases In Proceedings of "Proccedings of the Third Conference on CLIPS", September 12-14, 1994, Tx. PDF

Pazzani, M., Merz, C., Murphy, P., Ali, K., Hume, T., & Brunk, C. (1994). Reducing Misclassification Costs. Proceedings of the 11th International Conference of Machine Learning, New Brunswick. Morgan Kaufmann, 217-225. PDF Slides HTML Postscript

Ali, K., Brunk, C., & Pazzani, M. (1994). On Learning Multiple Descriptions of a Concept. In Proceedings of the Sixth International Conference on Tools with Artificial Intelligence. New Orleans, LA: IEEE Press. PDF Postscript

Merz, C., & Pazzani, M. (1994). Parameter Tuning for the MAX Expert System, In Proceedings of the Sixth International Conference on Tools with Artificial Intelligence. New Orleans, LA: IEEE Press. pp. 632-639. PDF

Pazzani, M., Murphy, P., Ali, K., & Schulenburg, D. (1994). Trading off coverage for accuracy in forecasts: Applications to clinical data analysis. AAAI Symposium on AI in Medicine (pp. 106-110). Stanford, CA. Overheads PDF PDF

Pazzani, M. (1994). Guest Editorial "Computational models of human learning". Machine Learning, 12. PDF

Murphy, P., & Pazzani, M. (1994). Exploring the decision forest: An empirical investigation of OCCAM's razor in decision tree induction. Journal of Artificial Intelligence, 1, 257-275. PDF Postscript

Giovanni Semeraro, Floriana Esposito, Donato Malerba, Clifford Brunk, Michael Pazzani: (1994) Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL. In Laurent Fribourg, Franco Turini (Eds.): Logic Programming Synthesis and Transformation - Meta-Programming in Logic. 4th Internation Workshops, LOPSTR'94 and META'94, Pisa, Italy, June 20-21, 1994, Proceedings. Lecture Notes in Computer Science, Vol. 883, Springer.

1993

Pazzani, M. (1993). Reply to Review of "Creating a memory of causal relationships". Machine Learning, 11. PDF

Yamazaki, Takefumi & Pazzani, Michael (1994). A Cluster Analysis Approach to Learning a Semantic Hierarchy for Machine Translation. ML-COLT '94 Workshop on Constructive Induction and Change of Representation. PDF Postscipt

Pazzani, M., & Brunk, C. (1993). Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. The National Conference on Artificial Intelligence (pp. 328-334). Washington, D.C: AAAI Press. Postscript Overheads PDF PDF

Ali, K., & Pazzani, M. (1993). HYDRA: A noise-tolerant relational concept learning algorithm. The International Joint Conference on Artificial Intelligence, Chambery, France. PDF Postscript

Wogulis, J., & Pazzani, M. (1993). A methodology for evaluating theory revision systems: Results with AUDREY II. The International Joint Conference on Artificial Intelligence, Chambery, France. PDF Postscript

Murphy, P., & Pazzani, M. (1993). Exploring the decision forest. Computational Learning and Natural Learning, Provincetown, MA PDF Postscript

Pazzani, M. (1993). Learning causal patterns: Making a transition from data-driven to theory-driven learning. Machine Learning, 11, 173-194 PDF.

1992

Ali K. and Pazzani M. (1992). Reducing the small disjuncts problem by learning probabilistic concept descriptions. In Petsche, T., Hanson, S.J. & Shavlik, J. (Eds), Computational Learning Theory and Natural Learning Systems, Vol. 3. Cambridge, Massachusetts. MIT Press. PDF

Brunk, C. & Pazzani, M. (1992). Knowledge Acquisition with a Knowledge-Intensive Machine Learning System. Proceedings of the Seventh Knowledge Acquisition for Knowledge-Based Systems Workshop. (4.1-4.20) Banff, Alberta: SRDG Publications. PDF Postscript

Pazzani, M. (1992). When Prior Knowledge Hinders Learning. AAAI Workshop on Constraining learning with Prior Knowledge. San Jose, CA. PDF

Pazzani, M., & Kibler, D. (1992). The utility of prior knowledge in inductive learning. Machine Learning, 9 , 54-97. PDF

Pazzani, M., & Sarrett, W. (1992). A framework for average case analysis of conjunctive learning algorithms. Machine Learning, 9, 349-372. PDF

Pazzani, M., Brunk, C., & Silverstein, G. (1992). A information-based approach to combining empirical and explanation-based learning. In S. Muggleton (Ed.). Inductive Logic Programming. (pp. 373-394). London: Academic Press.

Hirschberg, D., & Pazzani, M. (1992). Average case analysis of k-CNF learning algorithms. Proceedings of the Tenth International Conference on Machine Learning (pp. 206-211). Aberdeen, Scotland: Morgan Kaufmann. PDF

1991

Hirschberg, D., Pazzani, M., & Ali, K. (1991). Average case analysis of k-CNF and k-DNF learning algorithms. Second International Workshop on Computational Learning Theory and Natural Learning Systems: Constraints and Prospects. Berkeley, CA. PDF Postscript of Final 1995 version

Fisher, D., & Pazzani, M. (1991). Computational models of concept learning. In D. Fisher, M. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.

Fisher, D., Pazzani, M., & Langley, P. (1991). Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann. Order this book from the publisher

Pazzani, M. (1991). A computational theory of learning causal relationships. Cognitive Science, 15, 401-424. PDF

Pazzani, M. (1991). Learning to predict and explain: An integration of similarity-based, theory-driven and explanation-based learning. Journal of the Learning Sciences, 1, 2, 153-199.

Pazzani, M. (1991). The influence of prior knowledge on concept acquisition: Experimental and computational results. Journal of Experimental Psychology: Learning, Memory & Cognition, 17, 3, 416-432. PDF Preprint PDF Final

Pazzani, M., & Brunk, C. (1991). Detecting and correcting errors in rule-based expert systems: an integration of empirical and explanation-based learning. Knowledge Acquisition, 3, 157-173. PDF

Pazzani, M., Brunk, C., & Silverstein, G. (1991). A knowledge-intensive approach to learning relational concepts. Proceedings of the Eighth International Workshop on Machine Learning (pp. 432-436). Evanston, IL: Morgan Kaufmann.

Silverstein, G., & Pazzani, M. (1991). Relational clichés: Constraining constructive induction during relational learning. Proceedings of the Eighth International Workshop on Machine Learning (pp. 203-207). Evanston, IL: Morgan Kaufmann. PDF

Cain, T., Pazzani, M., & Silverstein, G. (1991). Using domain knowledge to influence similarity judgments. Proceedings of the Case-Based Reasoning Workshop. Washington, DC: Morgan Kaufmann. Overheads PDF

Brunk, C., & Pazzani, M. (1991). An investigation of noise tolerant relational learning algorithms. Proceedings of the Eighth International Workshop on Machine Learning (pp. 389-391). Evanston, IL: Morgan Kaufmann. Postscript PDF

Murphy, P., & Pazzani, M. (1991). ID2-of-3: Constructive induction of m-of-n discriminators for decision trees. Proceedings of the Eighth International Workshop on Machine Learning (pp. 183-187). Evanston, IL: Morgan Kaufmann. PDF Postscript

Fisher, D., & Pazzani, M. (1991). Theory-guided concept formation. In D. Fisher, M. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.

Fisher, D., & Pazzani, M. (1991). Concept formation in context. In D. Fisher, M. Pazzani, & P. Langley (Eds.), Concept formation: Knowledge and experience in unsupervised learning. San Mateo, CA: Morgan Kaufmann.

1990

Pazzani, M. J., & Brunk, C. A. (1990). Detecting and correcting errors in rule-based expert systems: an integration of empirical and explanation-based learning. Knowledge Acquisition for Knowledge-Based Systems Workshop. PDF of 1991 journal version

Pazzani, M., & Silverstein, G. (1990). Feature selection and hypothesis selection: Models of induction. Proceedings of the Twelfth Annual Conference of the Cognitive Science Society. (pp. 221-228). Cambridge, MA: Lawrence Erlbaum. PDF

Pazzani, M. (1990). Creating a memory of causal relationships: An integration of empirical and explanation-based learning methods. Hillsdale, NJ: Lawrence Erlbaum Associates. Order this book from the publisher

Pazzani, M. (1990). Learning in order to avoid search in logic programming. Computers and Mathematics with Applications, 2, 10, 101-110.

Pazzani M., & Dyer, M. (1990). Memory organization and explanation-based learning. International Journal of Expert Systems, 2, 3, 331-358.

Pazzani, M., & Flowers, M. (1990). Scientific discovery in the layperson. In J. Shrager & P. Langley (Eds.), Computational models of scientific discovery and theory formation. San Mateo, CA: Morgan Kaufmann. PDF

Billman, D., Fisher, D., Gluck, M., Langley, P., & Pazzani, M. (1990). Computational models of category learning. Proceedings of the Twelfth Annual Conference of the Cognitive Science Society. (pp. 989-996). Cambridge, MA: Lawrence Erlbaum.

1989

Pazzani, M. (1989). Indexing strategies for goal specific retrieval of cases. Proceedings of the Case-Based Reasoning Workshop (pp. 31-35). Pensacola Beach, FL: Morgan Kaufmann. Overheads PDF PDF

Pazzani, M. (1989). Explanation-based learning with weak domain theories. Proceedings of the Sixth International Workshop on Machine Learning (pp. 72-74). Ithaca, NY: Morgan Kaufmann. PDF

Pazzani, M. (1989). Learning from historical precedent. Artificial Intelligence Systems in Government Conference. (pp. 150-156). Washington, DC. Overheads PDF

Pazzani, M. (1989). Explanation-based learning of diagnostic heuristics: A comparison of learning from success and failure. Artificial Intelligence Systems in Government Conference (pp. 164-169). Washington DC.

Pazzani, M., & Schulenburg, D. (1989). The influence of prior theories on the ease of concept acquisition. Proceedings of the Eleventh Annual Conference of the Cognitive Science Society (pp. 812-819). Ann Arbor, MI: Lawrence Erlbaum PDF

Pazzani, M. (1989). Detecting and correcting errors of omission after explanation-based learning. Proceedings of the Eleventh International Joint Conference on Artificial Intelligence (pp. 713-718). Detroit, MI: Morgan Kaufmann. PDF Overheads PDF

Pazzani, M. (1989). Learning fault diagnosis heuristics from device descriptions. In Y. Kodratoff & R. Michalski (Eds.), Machine Learning: An artificial intelligence approach (Vol. III). San Mateo, CA: Morgan Kaufmann.

Pazzani, M., & Sarrett, W. (1989). Average case analysis of conjunctive learning algorithms. Proceedings of the Seventh International Conference on Machine Learning (pp. 339-347). Austin, TX: Morgan Kaufmann. PDF of 1992 Journal Version

Sarrett, W., & Pazzani, M. (1989). One-sided algorithms for integrating empirical and explanation-based learning. Proceedings of the Sixth International Workshop on Machine Learning (pp. 26-28). Ithaca, NY: Morgan Kaufmann. PDF

Schulenburg, D., & Pazzani, M. (1989). Explanation-based learning of indirect speech act interpretation rules. Proceedings of the First International Lexical Acquisition Workshop. Detroit, MI. PDF

Pazzani, M. (1989). Creating high-level knowledge structures from simple elements. In K. Morik (Ed.), Knowledge representation and organization in machine learning, Lecture notes in Artificial Intelligence, No 347. New York: Springer-Verlag.

1988

Pazzani, M. (1988). Explanation-based learning for knowledge-based systems. In B. Gaines & J. Boose (Eds.), Knowledge acquisition for knowledge-based systems (pp. 215-238). London: Academic Press.

Pazzani, M. (1988). Selecting the best explanation for explanation-based learning. AAAI Symposium on Explanation-Based Learning (pp. 156-170). Stanford University. PDF

Pazzani, M. (1988). Learning during plan recognition. AAAI Workshop on Plan Recognition. (pp. 1-5). St. Paul, MN.

Pazzani, M. (1988). Integrated learning with incorrect and incomplete theories. Proceedings of the Fifth International Conference on Machine Learning (pp. 291-298). Ann Arbor, MI: Morgan Kaufmann.

Pazzani, M. (1988). Integrating empirical and explanation-based learning methods in OCCAM. Proceedings of the Third European Working Session on Learning (pp. 147-166). Glasgow, Scotland: Pitman.

1987

Pazzani, M. (1987). Creating high-level knowledge structures from primitive elements. Knowledge Representation and Knowledge Organization in Machine Learning Workshop. Geseke, Germany.

Pazzani, M. (1987). Inducing causal and social theories: a prerequisite for explanation-based learning. Proceedings of the Fourth International Workshop on Machine Learning (pp. 230-241). Irvine, CA: Morgan Kaufmann. PDF

Pazzani, M., Dyer, M., & Flowers, M. (1987). Using prior learning to facilitate the learning of new causal theories. Proceedings of the Tenth International Joint Conference on Artificial Intelligence. (pp. 277-279). Milan, Italy: Morgan Kaufmann. PDF

Pazzani, M., & Dyer, M. (1987). A comparison of concept identification in human learning and network learning with the generalized delta rule. Proceedings of the Tenth International Joint Conference on Artificial Intelligence (pp. 147-151). Milan, Italy: Morgan Kaufmann. PDF

Pazzani, M. (1987). Failure-driven learning of fault diagnosis heuristics. IEEE Transactions on Systems, Man and Cybernetics: Special issue on Causal and Strategic Aspects of Diagnostic Reasoning, 17, 3, 380-394.

Pazzani, M. (1987). Explanation-based learning for knowledge-based systems. International Journal of Man-Machine Studies, 26, 413-433.

1986

Pazzani, M., & Brindle. A. (1986). Automated diagnosis of attitude control anomalies. Proceedings of the Annual AAS Guidance and Control Conference. Keystone, CO: American Astronautical Society. PDF

Pazzani, M. (1986). Refining the knowledge base of a diagnostic expert system: An application of failure-driven learning. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 1029-1035). Philadelphia, PA: Morgan Kaufmann. PDF

Pazzani, M., Dyer, M., & Flowers, M. (1986). The role of prior causal theories in generalization. Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 545-550). Philadelphia, PA: Morgan Kaufmann. PDF

1985

Pazzani, M. (1985). Explanation and generalization-based memory. Proceedings of the Seventh Annual Conference of the Cognitive Society Conference (pp. 323-328). Irvine, CA: Lawrence Erlbaum.

1984

Cullingford, R., & Pazzani, M. (1984). Word-meaning selection in multiprocess language understanding programs. IEEE Transactions on Pattern Analysis and Machine Intelligence 6,4, 493-509. PDF

Pazzani, M. (1984). Conceptual analysis of garden-path sentences. Proceedings of the Tenth International Conference on Computational Linguistics (pp. 486-490). Stanford, CA. PDF

1983

Pazzani, M. (1983). Interactive script instantiation. Proceedings of the National Conference on Artificial Intelligence (pp. 320-326). Washington DC: Morgan Kaufmann. PDF


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