2011
106Active learning using on-line algorithms. Chris Mesterharm, Michael J. Pazzani. KDD 2011, 850-858. Web SearchBibTeXDownload
2010
105An energy-efficient mobile recommender system. Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, Michael J. Pazzani. KDD 2010, 899-908. Web SearchBibTeXDownload
2007
104Adaptive News Access. Daniel Billsus, Michael J. Pazzani. The Adaptive Web 2007, 550-570. Web SearchBibTeXDownload
103Content-Based Recommendation Systems. Michael J. Pazzani, Daniel Billsus. The Adaptive Web 2007, 325-341. Web SearchBibTeXDownload
2006
102Adaptive Info - Personalizing the Wireless Web: Machine Learning 275 and Business 101. Michael J. Pazzani. AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications 2006, 12. Web SearchBibTeXDownload
101Mining for proposal reviewers: lessons learned at the national science foundation. Seth Hettich, Michael J. Pazzani. KDD 2006, 862-871. Web SearchBibTeXDownload
2005
100The Lowell database research self-assessment. 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. Commun. ACM (48): 111-118 (2005). Cited by 2Web SearchBibTeXDownload
2004
99Machine Learning for Personalized Wireless Portals. Michael J. Pazzani. ICTAI 2004, 3. Web SearchBibTeXDownload
2003
98The Lowell Database Research Self Assessment. 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. CoRR (cs.DB/0310006) (2003). Web SearchBibTeXDownload
97Adaptive Interfaces for Ubiquitous Web Access. Michael J. Pazzani. User Modeling 2003, 1. Web SearchBibTeXDownload
2002
96Locally adaptive dimensionality reduction for indexing large time series databases. Kaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani. ACM Trans. Database Syst. (27): 188-228 (2002). Cited by 478Web SearchBibTeXDownload
95Adaptive Web Site Agents. Michael J. Pazzani, Daniel Billsus. Autonomous Agents and Multi-Agent Systems (5): 205-218 (2002). Web SearchBibTeXDownload
94Adaptive interfaces for ubiquitous web access. Daniel Billsus, Clifford Brunk, Craig Evans, Brian Gladish, Michael J. Pazzani. Commun. ACM (45): 34-38 (2002). Web SearchBibTeXDownload
93Learning the Structure of Augmented Bayesian Classifiers. Eamonn J. Keogh, Michael J. Pazzani. International Journal on Artificial Intelligence Tools (11): 587-601 (2002). Cited by 31Web SearchBibTeXDownload
92Commercial Applications of Machine Learning for Personalized Wireless Portals. Michael J. Pazzani. PRICAI 2002, 1-5. Web SearchBibTeXDownload
91Iterative Deepening Dynamic Time Warping for Time Series. Selina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani. SDM 2002. Cited by 93Web SearchBibTeXDownload
2001
90Detecting Group Differences: Mining Contrast Sets. Stephen D. Bay, Michael J. Pazzani. Data Min. Knowl. Discov. (5): 213-246 (2001). Web SearchBibTeXDownload
89An Online Algorithm for Segmenting Time Series. Eamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani. ICDM 2001, 289-296. Cited by 275Web SearchBibTeXDownload
88Ensemble-index: a new approach to indexing large databases. Eamonn J. Keogh, Selina Chu, Michael J. Pazzani. KDD 2001, 117-125. Cited by 5Web SearchBibTeXDownload
87Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra. Knowl. Inf. Syst. (3): 263-286 (2001). Cited by 406Web SearchBibTeXDownload
86Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani. SIGMOD Conference 2001, 151-162. Web SearchBibTeXDownload
85Machine Learning for User Modeling. Geoffrey I. Webb, Michael J. Pazzani, Daniel Billsus. User Model. User-Adapt. Interact. (11): 19-29 (2001). Web SearchBibTeXDownload
84Improving mobile internet usability. George Buchanan, Sarah Farrant, Matt Jones, Harold W. Thimbleby, Gary Marsden, Michael J. Pazzani. WWW 2001, 673-680. Web SearchBibTeXDownload
2000
83Characterizing Model Erros and Differences. Stephen D. Bay, Michael J. Pazzani. ICML 2000, 49-56. Web SearchBibTeX
82Knowledge discovery from data?. Michael J. Pazzani. IEEE Intelligent Systems (15): 10-13 (2000). Web SearchBibTeXDownload
81Representation of electronic mail filtering profiles: a user study. Michael J. Pazzani. IUI 2000, 202-206. Web SearchBibTeXDownload
80A learning agent for wireless news access. Daniel Billsus, Michael J. Pazzani, James Chen. IUI 2000, 33-36. Web SearchBibTeXDownload
79Scaling up dynamic time warping for datamining applications. Eamonn J. Keogh, Michael J. Pazzani. KDD 2000, 285-289. Cited by 158Web SearchBibTeXDownload
78Learning with Globally Predictive Tests. Michael J. Pazzani. New Generation Comput. (18): 28-38 (2000). Web SearchBibTeXDownload
77A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. Eamonn J. Keogh, Michael J. Pazzani. PAKDD 2000, 122-133. Cited by 3Web SearchBibTeXDownload
76Collaborative Filtering with the Simple Bayesian Classifier. Koji Miyahara, Michael J. Pazzani. PRICAI 2000, 679-689. Web SearchBibTeXDownload
75The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. Stephen D. Bay, Dennis F. Kibler, Michael J. Pazzani, Padhraic Smyth. SIGKDD Explorations (2): 81-85 (2000). Web SearchBibTeXDownload
74User Modeling for Adaptive News Access. Daniel Billsus, Michael J. Pazzani. User Model. User-Adapt. Interact. (10): 147-180 (2000). Web SearchBibTeXDownload
1999
73Adaptive Web Site Agents. Michael J. Pazzani, Daniel Billsus. Agents 1999, 394-395. Web SearchBibTeXDownload
72A Personal News Agent That Talks, Learns and Explains. Daniel Billsus, Michael J. Pazzani. Agents 1999, 268-275. Web SearchBibTeXDownload
71Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles. AI Magazine (20): 13-25 (1999). Web SearchBibTeXDownload
70Refinement of Neuro-psychological Tests for Dementia Screening in a Cross Cultural Population Using Machine Learning. Subramani Mani, Malcolm B. Dick, Michael J. Pazzani, Evelyn L. Teng, Daniel Kempler, I. Maribell Taussig. AIMDM 1999, 326-335. Web SearchBibTeXDownload
69Two-Stage Machine Learning model for guideline development. Subramani Mani, William Rodman Shankle, Malcolm B. Dick, Michael J. Pazzani. Artificial Intelligence in Medicine (16): 51-71 (1999). Web SearchBibTeXDownload
68A Framework for Collaborative, Content-Based and Demographic Filtering. Michael J. Pazzani. Artif. Intell. Rev. (13): 393-408 (1999). Web SearchBibTeXDownload
67Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses. Richard H. Lathrop, Michael J. Pazzani. J. Comb. Optim. (3): 301-320 (1999). Web SearchBibTeXDownload
66Detecting Change in Categorical Data: Mining Contrast Sets. Stephen D. Bay, Michael J. Pazzani. KDD 1999, 302-306. Web SearchBibTeXDownload
65A Principal Components Approach to Combining Regression Estimates. Christopher J. Merz, Michael J. Pazzani. Machine Learning (36): 9-32 (1999). Web SearchBibTeXDownload
64Scaling up Dynamic Time Warping to Massive Dataset. Eamonn J. Keogh, Michael J. Pazzani. PKDD 1999, 1-11. Cited by 102Web SearchBibTeXDownload
63Relevance Feedback Retrieval of Time Series Data. Eamonn J. Keogh, Michael J. Pazzani. SIGIR 1999, 183-190. Cited by 71Web SearchBibTeXDownload
62Workshop on Recommender Systems: Algorithms and Evaluation. Ian Soboroff, Charles K. Nicholas, Michael J. Pazzani. SIGIR Forum (33): 36-43 (1999). Web SearchBibTeXDownload
61An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. Eamonn J. Keogh, Michael J. Pazzani. SSDBM 1999, 56-67. Cited by 53Web SearchBibTeXDownload
1998
60Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles. AAAI/IAAI 1998, 1071-1078. Web SearchBibTeXDownload
59Adjusted Probability Naive Bayesian Induction. Geoffrey I. Webb, Michael J. Pazzani. Australian Joint Conference on Artificial Intelligence 1998, 285-295. Web SearchBibTeXDownload
58Learning with Globally Predictive Tests. Michael J. Pazzani. Discovery Science 1998, 220-231. Web SearchBibTeXDownload
57Learning Collaborative Information Filters. Daniel Billsus, Michael J. Pazzani. ICML 1998, 46-54. Web SearchBibTeX
56An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. Eamonn J. Keogh, Michael J. Pazzani. KDD 1998, 239-243. Cited by 288Web SearchBibTeXDownload
1997
55Learning Probabilistic User Profiles: Applications for Finding Interesting Web Sites, Notifying Users of Relevant Changes to Web Pages, and Locating Grant Opportunities. 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. AI Magazine (18): 47-56 (1997). Web SearchBibTeXDownload
54Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. William Rodman Shankle, Subramani Mani, Michael J. Pazzani, Padhraic Smyth. AIME 1997, 73-85. Web SearchBibTeXDownload
53Knowledge Discovery from a Breast Cancer Database. Subramani Mani, Michael J. Pazzani, John West. AIME 1997, 130-133. Web SearchBibTeXDownload
52Beyond Concise and Colorful: Learning Intelligible Rules. Michael J. Pazzani, Subramani Mani, William Rodman Shankle. KDD 1997, 235-238. Web SearchBibTeXDownload
51On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Pedro Domingos, Michael J. Pazzani. Machine Learning (29): 103-130 (1997). Web SearchBibTeXDownload
50Learning and Revising User Profiles: The Identification of Interesting Web Sites. Michael J. Pazzani, Daniel Billsus. Machine Learning (27): 313-331 (1997). Web SearchBibTeXDownload
49The Do-I-Care Agent: Effective Social Discovery and Filtering on the Web. Mark S. Ackerman, Brian Starr, Michael J. Pazzani. RIAO 1997, 17-32. Web SearchBibTeX
1996
48Syskill & Webert: Identifying Interesting Web Sites. Michael J. Pazzani, Jack Muramatsu, Daniel Billsus. AAAI/IAAI, Vol. 1 1996, 54-61. Web SearchBibTeXDownload
47Simple Bayesian Classifiers Do Not Assume Independence. Pedro Domingos, Michael J. Pazzani. AAAI/IAAI, Vol. 2 1996, 1386. Web SearchBibTeXDownload
46Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. Pedro Domingos, Michael J. Pazzani. ICML 1996, 105-112. Web SearchBibTeX
45Tuning Numeric Parameters to Troubleshoot a Telephone-Network Loop. Christopher J. Merz, Michael J. Pazzani, Andrea Pohoreckyj Danyluk. IEEE Expert (11): 44-49 (1996). Web SearchBibTeXDownload
44Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrac, Saso Dzeroski. Michael J. Pazzani. Machine Learning (23): 103-108 (1996). Web SearchBibTeXDownload
43Review of ``Inductive Logic Programming: Techniques and Applications'' by Nada Lavrac, Saso Dzeroski. Michael J. Pazzani. Machine Learning (23): 103-108 (1996). Web SearchBibTeX
42Error Reduction through Learning Multiple Descriptions. Kamal M. Ali, Michael J. Pazzani. Machine Learning (24): 173-202 (1996). Web SearchBibTeXDownload
41Combining Neural Network Regression Estimates with Regularized Linear Weights. Christopher J. Merz, Michael J. Pazzani. NIPS 1996, 564-570. Web SearchBibTeXDownload
1995
40A Lexical Based Semantic Bias for Theory Revision. Clifford Brunk, Michael J. Pazzani. ICML 1995, 81-89. Web SearchBibTeX
39Learning Hierarchies from Ambiguous Natural Language Data. Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz. ICML 1995, 575-583. Web SearchBibTeX
38An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers. Michael J. Pazzani. KDD 1995, 228-233. Web SearchBibTeXDownload
37Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique. Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz. Learning for Natural Language Processing 1995, 329-342. Web SearchBibTeXDownload
1994
36Revision of Production System Rule-Bases. Patrick M. Murphy, Michael J. Pazzani. ICML 1994, 199-207. Web SearchBibTeX
35Reducing Misclassification Costs. Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal Ali, Timothy Hume, Clifford Brunk. ICML 1994, 217-225. Web SearchBibTeX
34On Learning Multiple Descriptions of a Concept. Kamal Ali, Clifford Brunk, Michael J. Pazzani. ICTAI 1994, 476-483. Web SearchBibTeX
33Parameter Tuning for the MAX Expert System. Christopher J. Merz, Michael J. Pazzani. ICTAI 1994, 632-639. Web SearchBibTeX
32Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction. Patrick M. Murphy, Michael J. Pazzani. J. Artif. Intell. Res. (JAIR) (1): 257-275 (1994). Web SearchBibTeXDownload
31Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL. Giovanni Semeraro, Floriana Esposito, Donato Malerba, Clifford Brunk, Michael J. Pazzani. LOPSTR 1994, 183-198. Web SearchBibTeXDownload
30Guest Editor's Introduction. Sally A. McKee, Saumya K. Debray, Manuel V. Hermenegildo, Michael J. Maher. Machine Learning (16): 7-9 (1994). Web SearchBibTeXDownload
1993
29Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. Michael J. Pazzani, Clifford Brunk. AAAI 1993, 328-334. Web SearchBibTeXDownload
28A Methodology for Evaluating Theory Revision Systems: Results with Audrey II. James Wogulis, Michael J. Pazzani. IJCAI 1993, 1128-1134. Web SearchBibTeX
27HYDRA: A Noise-tolerant Relational Concept Learning Algorithm. Kamal M. Ali, Michael J. Pazzani. IJCAI 1993, 1064-1071. Web SearchBibTeX
26A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships. Michael J. Pazzani. Machine Learning (10): 185-190 (1993). Web SearchBibTeXDownload
25Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning. Michael J. Pazzani. Machine Learning (11): 173-194 (1993). Web SearchBibTeXDownload
1992
24The Utility of Knowledge in Inductive Learning. Michael J. Pazzani, Dennis F. Kibler. Machine Learning (9): 57-94 (1992). Web SearchBibTeXDownload
23A Framework for Average Case Analysis of Conjunctive Learning Algorithms. Michael J. Pazzani, Wendy Sarrett. Machine Learning (9): 349-372 (1992). Web SearchBibTeXDownload
22Average Case Analysis of Learning kappa-CNF Concepts. Daniel S. Hirschberg, Michael J. Pazzani. ML 1992, 206-211. Web SearchBibTeX
1991
21A Computational Theory of Learning Causal Relationships. Michael J. Pazzani. Cognitive Science (15): 401-424 (1991). Web SearchBibTeXDownload
20Constructive Induction of M-of-N Terms. Patrick M. Murphy, Michael J. Pazzani. ML 1991, 183-187. Web SearchBibTeX
19An Investigation of Noise-Tolerant Relational Concept Learning Algorithms. Clifford Brunk, Michael J. Pazzani. ML 1991, 389-393. Web SearchBibTeX
18Relational Clichés: Constraining Induction During Relational Learning. Glenn Silverstein, Michael J. Pazzani. ML 1991, 203-207. Web SearchBibTeX
17A Knowledge-intensive Approach to Learning Relational Concepts. Michael J. Pazzani, Clifford Brunk, Glenn Silverstein. ML 1991, 432-436. Web SearchBibTeX
1990
16Average Case Analysis of Conjunctive Learning Algorithms. Michael J. Pazzani, Wendy Sarrett. ML 1990, 339-347. Web SearchBibTeX
1989
15Detecting and Correcting Errors of Omission After Explanation-Based Learning. Michael J. Pazzani. IJCAI 1989, 713-718. Web SearchBibTeX
14One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning. Wendy Sarrett, Michael J. Pazzani. ML 1989, 26-28. Web SearchBibTeX
13Explanation-Based Learning with Week Domain Theories. Michael J. Pazzani. ML 1989, 72-74. Web SearchBibTeX
1988
12Integrating Explanation-Based and Empirical Learning Methods in OCCAM. Michael J. Pazzani. EWSL 1988, 147-165. Web SearchBibTeX
11Integrated Learning with Incorrect and Incomplete Theories. Michael J. Pazzani. ML 1988, 291-297. Web SearchBibTeX
1987
10A Comparison of Concept Identification in Human Learning and Network Learning with the Generalized Delta Rule. Michael J. Pazzani, Michael G. Dyer. IJCAI 1987, 147-150. Web SearchBibTeX
9Using Prior Learning to Facilitate the Learning of New Causal Theories. Michael J. Pazzani, Michael G. Dyer, Margot Flowers. IJCAI 1987, 277-279. Web SearchBibTeX
8Explanation-Based Learning for Knowledge-Based Systems. Michael J. Pazzani. International Journal of Man-Machine Studies (26): 413-433 (1987). Web SearchBibTeXDownload
7Creating High Level Knowledge Structures from Simple Elements. Michael J. Pazzani. Knowledge Representation and Organization in Machine Learning 1987, 258-288. Web SearchBibTeXDownload
1986
6Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning. Michael J. Pazzani. AAAI 1986, 1029-1035. Web SearchBibTeXDownload
5The Role of Prior Causal Theories in Generalization. Michael J. Pazzani, Michael G. Dyer, Margot Flowers. AAAI 1986, 545-550. Web SearchBibTeXDownload
1984
4Conceptual Analysis of Garden-Path Sentences. Michael J. Pazzani. COLING 1984, 486-490. Web SearchBibTeXDownload
3Word-Meaning Selection in Multiprocess Language Understanding Programs. Richard E. Cullingford, Michael J. Pazzani. IEEE Trans. Pattern Anal. Mach. Intell. (6): 493-509 (1984). Web SearchBibTeXDownload
1983
2Interactive Script Instantiation. Michael J. Pazzani. AAAI 1983, 320-326. Web SearchBibTeXDownload
1Knowledge Based Question Answering. Michael J. Pazzani, Carl Engelman. ANLP 1983, 73-80. Web SearchBibTeXDownload
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References
1. ^ UT-Austin Data Mining Seminar Schedule Abstracts - Retrieved 2010-09-28 - details
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