Michael J. Pazzani

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

Pearl Pu, Michael J. Pazzani, Elisabeth André, Doug Riecken (Eds.): Proceedings of the 2011 International Conference on Intelligent User Interfaces, February 13-16, 2011, Palo Alto, CA, USA. ACM 2011, isbn 978-1-4503-0419-1

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

Michael J. Pazzani, Daniel Billsus: Content-Based Recommendation Systems. The Adaptive Web 2007: 325-341

Daniel Billsus, Michael J. Pazzani: Adaptive News Access. The Adaptive Web 2007: 550-570

Michael J. Pazzani: Adaptive Info - Personalizing the Wireless Web: Machine Learning 275 and Business 101. AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications 2006: 12

Seth Hettich, Michael J. Pazzani: Mining for proposal reviewers: lessons learned at the national science foundation. KDD 2006: 862-871

Serge Abiteboul, Rakesh Agrawal, Philip A. Bernstein, Michael J. Carey, Stefano Ceri, W. Bruce Croft, David J. DeWitt, Michael J. Franklin, Hector Garcia-Molina, Dieter Gawlick, Jim Gray, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, Martin L. Kersten, Michael J. Pazzani, Michael Lesk, David Maier, Jeffrey F. Naughton, Hans-Jörg Schek, Timos K. Sellis, Avi Silberschatz, Michael Stonebraker, Richard T. Snodgrass, Jeffrey D. Ullman, Gerhard Weikum, Jennifer Widom, Stanley B. Zdonik: The Lowell database research self-assessment. Commun. ACM 48(5): 111-118 (2005)

Michael J. Pazzani: Machine Learning for Personalized Wireless Portals. ICTAI 2004: 3

Michael J. Pazzani: Adaptive Interfaces for Ubiquitous Web Access. User Modeling 2003: 1

Serge Abiteboul, Rakesh Agrawal, Philip A. Bernstein, Michael J. Carey, Stefano Ceri, W. Bruce Croft, David J. DeWitt, Michael J. Franklin, Hector Garcia-Molina, Dieter Gawlick, Jim Gray, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, Martin L. Kersten, Michael J. Pazzani, Michael Lesk, David Maier, Jeffrey F. Naughton, Hans-Jörg Schek, Timos K. Sellis, Avi Silberschatz, Michael Stonebraker, Richard T. Snodgrass, Jeffrey D. Ullman, Gerhard Weikum, Jennifer Widom, Stanley B. Zdonik: The Lowell Database Research Self Assessment. CoRR cs.DB/0310006 (2003)

Michael J. Pazzani, Daniel Billsus: Adaptive Web Site Agents. Autonomous Agents and Multi-Agent Systems 5(2): 205-218 (2002)

Daniel Billsus, Clifford Brunk, Craig Evans, Brian Gladish, Michael J. Pazzani: Adaptive interfaces for ubiquitous web access. Commun. ACM 45(5): 34-38 (2002)

Eamonn J. Keogh, Michael J. Pazzani: Learning the Structure of Augmented Bayesian Classifiers. International Journal on Artificial Intelligence Tools 11(4): 587-601 (2002)

Kaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. 27(2): 188-228 (2002)

Michael J. Pazzani: Commercial Applications of Machine Learning for Personalized Wireless Portals. PRICAI 2002: 1-5

Selina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani: Iterative Deepening Dynamic Time Warping for Time Series. SDM 2002

Stephen D. Bay, Michael J. Pazzani: Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl. Discov. 5(3): 213-246 (2001)

Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra: Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowl. Inf. Syst. 3(3): 263-286 (2001)

Geoffrey I. Webb, Michael J. Pazzani, Daniel Billsus: Machine Learning for User Modeling. User Model. User-Adapt. Interact. 11(1-2): 19-29 (2001)

Eamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani: An Online Algorithm for Segmenting Time Series. ICDM 2001: 289-296

Eamonn J. Keogh, Selina Chu, Michael J. Pazzani: Ensemble-index: a new approach to indexing large databases. KDD 2001: 117-125

Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. SIGMOD Conference 2001: 151-162

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

Michael J. Pazzani: Knowledge discovery from data? IEEE Intelligent Systems 15(2): 10-13 (2000)

Michael J. Pazzani: Learning with Globally Predictive Tests. New Generation Comput. 18(1): 28-38 (2000)

Stephen D. Bay, Dennis F. Kibler, Michael J. Pazzani, Padhraic Smyth: The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. SIGKDD Explorations 2(2): 81-85 (2000)

Daniel Billsus, Michael J. Pazzani: User Modeling for Adaptive News Access. User Model. User-Adapt. Interact. 10(2-3): 147-180 (2000)

Stephen D. Bay, Michael J. Pazzani: Characterizing Model Erros and Differences. ICML 2000: 49-56

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

Michael J. Pazzani: Representation of electronic mail filtering profiles: a user study. IUI 2000: 202-206

Eamonn J. Keogh, Michael J. Pazzani: Scaling up dynamic time warping for datamining applications. KDD 2000: 285-289

Eamonn J. Keogh, Michael J. Pazzani: A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. PAKDD 2000: 122-133

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

Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles: Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. AI Magazine 20(1): 13-25 (1999)

Michael J. Pazzani: A Framework for Collaborative, Content-Based and Demographic Filtering. Artif. Intell. Rev. 13(5-6): 393-408 (1999)

Subramani Mani, William Rodman Shankle, Malcolm B. Dick, Michael J. Pazzani: Two-Stage Machine Learning model for guideline development. Artificial Intelligence in Medicine 16(1): 51-71 (1999)

Richard H. Lathrop, Michael J. Pazzani: Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses. J. Comb. Optim. 3(2-3): 301-320 (1999)

Christopher J. Merz, Michael J. Pazzani: A Principal Components Approach to Combining Regression Estimates. Machine Learning 36(1-2): 9-32 (1999)

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

Daniel Billsus, Michael J. Pazzani: A Personal News Agent That Talks, Learns and Explains. Agents 1999: 268-275

Michael J. Pazzani, Daniel Billsus: Adaptive Web Site Agents. Agents 1999: 394-395

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. AIMDM 1999: 326-335

Stephen D. Bay, Michael J. Pazzani: Detecting Change in Categorical Data: Mining Contrast Sets. KDD 1999: 302-306

Eamonn J. Keogh, Michael J. Pazzani: Scaling up Dynamic Time Warping to Massive Dataset. PKDD 1999: 1-11

Eamonn J. Keogh, Michael J. Pazzani: Relevance Feedback Retrieval of Time Series Data. SIGIR 1999: 183-190

Eamonn J. Keogh, Michael J. Pazzani: An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. SSDBM 1999: 56-67

Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles: Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. AAAI/IAAI 1998: 1071-1078

Geoffrey I. Webb, Michael J. Pazzani: Adjusted Probability Naive Bayesian Induction. Australian Joint Conference on Artificial Intelligence 1998: 285-295

Michael J. Pazzani: Learning with Globally Predictive Tests. Discovery Science 1998: 220-231

Daniel Billsus, Michael J. Pazzani: Learning Collaborative Information Filters. ICML 1998: 46-54

Eamonn J. Keogh, Michael J. Pazzani: An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. KDD 1998: 239-243

Mark S. Ackerman, Daniel Billsus, Scott Gaffney, Seth Hettich, Gordon Khoo, Dong Joon Kim, Raymond Klefstad, Charles Lowe, Alexius Ludeman, Jack Muramatsu, Kazuo Omori, Michael J. Pazzani, Douglas Semler, Brian Starr, Paul Yap: Learning Probabilistic User Profiles: Applications for Finding Interesting Web Sites, Notifying Users of Relevant Changes to Web Pages, and Locating Grant Opportunities. AI Magazine 18(2): 47-56 (1997)

Michael J. Pazzani, Daniel Billsus: Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27(3): 313-331 (1997)

Pedro Domingos, Michael J. Pazzani: On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning 29(2-3): 103-130 (1997)

William Rodman Shankle, Subramani Mani, Michael J. Pazzani, Padhraic Smyth: Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. AIME 1997: 73-85

Subramani Mani, Michael J. Pazzani, John West: Knowledge Discovery from a Breast Cancer Database. AIME 1997: 130-133

Michael J. Pazzani, Subramani Mani, William Rodman Shankle: Beyond Concise and Colorful: Learning Intelligible Rules. KDD 1997: 235-238

Mark S. Ackerman, Brian Starr, Michael J. Pazzani: The Do-I-Care Agent: Effective Social Discovery and Filtering on the Web. RIAO 1997: 17-32

Christopher J. Merz, Michael J. Pazzani, Andrea Pohoreckyj Danyluk: Tuning Numeric Parameters to Troubleshoot a Telephone-Network Loop. IEEE Expert 11(1): 44-49 (1996)

Michael J. Pazzani: Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrac, Saso Dzeroski. Machine Learning 23(1): 103-108 (1996)

Kamal M. Ali, Michael J. Pazzani: Error Reduction through Learning Multiple Descriptions. Machine Learning 24(3): 173-202 (1996)

Michael J. Pazzani, Jack Muramatsu, Daniel Billsus: Syskill & Webert: Identifying Interesting Web Sites. AAAI/IAAI, Vol. 1 1996: 54-61

Pedro Domingos, Michael J. Pazzani: Simple Bayesian Classifiers Do Not Assume Independence. AAAI/IAAI, Vol. 2 1996: 1386

Pedro Domingos, Michael J. Pazzani: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. ICML 1996: 105-112

Christopher J. Merz, Michael J. Pazzani: Combining Neural Network Regression Estimates with Regularized Linear Weights. NIPS 1996: 564-570

Clifford Brunk, Michael J. Pazzani: A Lexical Based Semantic Bias for Theory Revision. ICML 1995: 81-89

Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz: Learning Hierarchies from Ambiguous Natural Language Data. ICML 1995: 575-583

Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz: Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique. Learning for Natural Language Processing 1995: 329-342

Michael J. Pazzani: An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers. KDD 1995: 228-233

Patrick M. Murphy, Michael J. Pazzani: Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction. J. Artif. Intell. Res. (JAIR) 1: 257-275 (1994)

Michael J. Pazzani: Guest Editor's Introduction. Machine Learning 16(1-2): 7-9 (1994)

Patrick M. Murphy, Michael J. Pazzani: Revision of Production System Rule-Bases. ICML 1994: 199-207

Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal Ali, Timothy Hume, Clifford Brunk: Reducing Misclassification Costs. ICML 1994: 217-225

Kamal Ali, Clifford Brunk, Michael J. Pazzani: On Learning Multiple Descriptions of a Concept. ICTAI 1994: 476-483

Christopher J. Merz, Michael J. Pazzani: Parameter Tuning for the MAX Expert System. ICTAI 1994: 632-639

Giovanni Semeraro, Floriana Esposito, Donato Malerba, Clifford Brunk, Michael J. Pazzani: Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL. LOPSTR 1994: 183-198

Michael J. Pazzani: A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships. Machine Learning 10: 185-190 (1993)

Michael J. Pazzani: Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning. Machine Learning 11: 173-194 (1993)

Michael J. Pazzani, Clifford Brunk: Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. AAAI 1993: 328-334

Kamal M. Ali, Michael J. Pazzani: HYDRA: A Noise-tolerant Relational Concept Learning Algorithm. IJCAI 1993: 1064-1071

James Wogulis, Michael J. Pazzani: A Methodology for Evaluating Theory Revision Systems: Results with Audrey II. IJCAI 1993: 1128-1134

Michael J. Pazzani, Dennis F. Kibler: The Utility of Knowledge in Inductive Learning. Machine Learning 9: 57-94 (1992)

Michael J. Pazzani, Wendy Sarrett: A Framework for Average Case Analysis of Conjunctive Learning Algorithms. Machine Learning 9: 349-372 (1992)

Daniel S. Hirschberg, Michael J. Pazzani: Average Case Analysis of Learning kappa-CNF Concepts. ML 1992: 206-211

Michael J. Pazzani: A Computational Theory of Learning Causal Relationships. Cognitive Science 15(3): 401-424 (1991)

Patrick M. Murphy, Michael J. Pazzani: Constructive Induction of M-of-N Terms. ML 1991: 183-187

Glenn Silverstein, Michael J. Pazzani: Relational Clichés: Constraining Induction During Relational Learning. ML 1991: 203-207

Clifford Brunk, Michael J. Pazzani: An Investigation of Noise-Tolerant Relational Concept Learning Algorithms. ML 1991: 389-393

Michael J. Pazzani, Clifford Brunk, Glenn Silverstein: A Knowledge-intensive Approach to Learning Relational Concepts. ML 1991: 432-436

Michael J. Pazzani: Creating a memory of causal relationships - an integration of empirical and explanation-based learning methods. Lawrence Erlbaum 1990, pp. 1-350

Michael J. Pazzani, Wendy Sarrett: Average Case Analysis of Conjunctive Learning Algorithms. ML 1990: 339-347

Wendy Sarrett, Michael J. Pazzani: One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning. ML 1989: 26-28

Michael J. Pazzani: Explanation-Based Learning with Week Domain Theories. ML 1989: 72-74

Michael J. Pazzani: Detecting and Correcting Errors of Omission After Explanation-Based Learning. IJCAI 1989: 713-718

Michael J. Pazzani: Integrating Explanation-Based and Empirical Learning Methods in OCCAM. EWSL 1988: 147-165

Michael J. Pazzani: Integrated Learning with Incorrect and Incomplete Theories. ML 1988: 291-297

Michael J. Pazzani: Explanation-Based Learning for Knowledge-Based Systems. International Journal of Man-Machine Studies 26(4): 413-433 (1987)

Michael J. Pazzani, Michael G. Dyer: A Comparison of Concept Identification in Human Learning and Network Learning with the Generalized Delta Rule. IJCAI 1987: 147-150

Michael J. Pazzani, Michael G. Dyer, Margot Flowers: Using Prior Learning to Facilitate the Learning of New Causal Theories. IJCAI 1987: 277-279

Michael J. Pazzani: Creating High Level Knowledge Structures from Simple Elements. Knowledge Representation and Organization in Machine Learning 1987: 258-288

Michael J. Pazzani, Michael G. Dyer, Margot Flowers: The Role of Prior Causal Theories in Generalization. AAAI 1986: 545-550

Michael J. Pazzani: Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning. AAAI 1986: 1029-1035

Richard E. Cullingford, Michael J. Pazzani: Word-Meaning Selection in Multiprocess Language Understanding Programs. IEEE Trans. Pattern Anal. Mach. Intell. 6(4): 493-509 (1984)

Michael J. Pazzani: Conceptual Analysis of Garden-Path Sentences. COLING 1984: 486-490

Michael J. Pazzani: Interactive Script Instantiation. AAAI 1983: 320-326

Michael J. Pazzani, Carl Engelman: Knowledge Based Question Answering. ANLP 1983: 73-80