| 2011 |
|---|
| 106 | Active learning using on-line algorithms. Chris Mesterharm, Michael J. Pazzani. KDD 2011, 850-858. Web SearchBibTeXDownload |
| 2010 |
|---|
| 105 | An 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 |
|---|
| 104 | Adaptive News Access. Daniel Billsus, Michael J. Pazzani. The Adaptive Web 2007, 550-570. Web SearchBibTeXDownload |
| 103 | Content-Based Recommendation Systems. Michael J. Pazzani, Daniel Billsus. The Adaptive Web 2007, 325-341. Web SearchBibTeXDownload |
| 2006 |
|---|
| 102 | Adaptive 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 |
| 101 | Mining for proposal reviewers: lessons learned at the national science foundation. Seth Hettich, Michael J. Pazzani. KDD 2006, 862-871. Web SearchBibTeXDownload |
| 2005 |
|---|
| 100 | The 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 |
|---|
| 99 | Machine Learning for Personalized Wireless Portals. Michael J. Pazzani. ICTAI 2004, 3. Web SearchBibTeXDownload |
| 2003 |
|---|
| 98 | The 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 |
| 97 | Adaptive Interfaces for Ubiquitous Web Access. Michael J. Pazzani. User Modeling 2003, 1. Web SearchBibTeXDownload |
| 2002 |
|---|
| 96 | Locally 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 |
| 95 | Adaptive Web Site Agents. Michael J. Pazzani, Daniel Billsus. Autonomous Agents and Multi-Agent Systems (5): 205-218 (2002). Web SearchBibTeXDownload |
| 94 | Adaptive interfaces for ubiquitous web access. Daniel Billsus, Clifford Brunk, Craig Evans, Brian Gladish, Michael J. Pazzani. Commun. ACM (45): 34-38 (2002). Web SearchBibTeXDownload |
| 93 | Learning 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 |
| 92 | Commercial Applications of Machine Learning for Personalized Wireless Portals. Michael J. Pazzani. PRICAI 2002, 1-5. Web SearchBibTeXDownload |
| 91 | Iterative Deepening Dynamic Time Warping for Time Series. Selina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani. SDM 2002. Cited by 93Web SearchBibTeXDownload |
| 2001 |
|---|
| 90 | Detecting Group Differences: Mining Contrast Sets. Stephen D. Bay, Michael J. Pazzani. Data Min. Knowl. Discov. (5): 213-246 (2001). Web SearchBibTeXDownload |
| 89 | An Online Algorithm for Segmenting Time Series. Eamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani. ICDM 2001, 289-296. Cited by 275Web SearchBibTeXDownload |
| 88 | Ensemble-index: a new approach to indexing large databases. Eamonn J. Keogh, Selina Chu, Michael J. Pazzani. KDD 2001, 117-125. Cited by 5Web SearchBibTeXDownload |
| 87 | Dimensionality 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 |
| 86 | Locally 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 |
| 85 | Machine Learning for User Modeling. Geoffrey I. Webb, Michael J. Pazzani, Daniel Billsus. User Model. User-Adapt. Interact. (11): 19-29 (2001). Web SearchBibTeXDownload |
| 84 | Improving mobile internet usability. George Buchanan, Sarah Farrant, Matt Jones, Harold W. Thimbleby, Gary Marsden, Michael J. Pazzani. WWW 2001, 673-680. Web SearchBibTeXDownload |
| 2000 |
|---|
| 83 | Characterizing Model Erros and Differences. Stephen D. Bay, Michael J. Pazzani. ICML 2000, 49-56. Web SearchBibTeX |
| 82 | Knowledge discovery from data?. Michael J. Pazzani. IEEE Intelligent Systems (15): 10-13 (2000). Web SearchBibTeXDownload |
| 81 | Representation of electronic mail filtering profiles: a user study. Michael J. Pazzani. IUI 2000, 202-206. Web SearchBibTeXDownload |
| 80 | A learning agent for wireless news access. Daniel Billsus, Michael J. Pazzani, James Chen. IUI 2000, 33-36. Web SearchBibTeXDownload |
| 79 | Scaling up dynamic time warping for datamining applications. Eamonn J. Keogh, Michael J. Pazzani. KDD 2000, 285-289. Cited by 158Web SearchBibTeXDownload |
| 78 | Learning with Globally Predictive Tests. Michael J. Pazzani. New Generation Comput. (18): 28-38 (2000). Web SearchBibTeXDownload |
| 77 | A 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 |
| 76 | Collaborative Filtering with the Simple Bayesian Classifier. Koji Miyahara, Michael J. Pazzani. PRICAI 2000, 679-689. Web SearchBibTeXDownload |
| 75 | The 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 |
| 74 | User Modeling for Adaptive News Access. Daniel Billsus, Michael J. Pazzani. User Model. User-Adapt. Interact. (10): 147-180 (2000). Web SearchBibTeXDownload |
| 1999 |
|---|
| 73 | Adaptive Web Site Agents. Michael J. Pazzani, Daniel Billsus. Agents 1999, 394-395. Web SearchBibTeXDownload |
| 72 | A Personal News Agent That Talks, Learns and Explains. Daniel Billsus, Michael J. Pazzani. Agents 1999, 268-275. Web SearchBibTeXDownload |
| 71 | Knowledge-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 |
| 70 | Refinement 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 |
| 69 | Two-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 |
| 68 | A Framework for Collaborative, Content-Based and Demographic Filtering. Michael J. Pazzani. Artif. Intell. Rev. (13): 393-408 (1999). Web SearchBibTeXDownload |
| 67 | Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses. Richard H. Lathrop, Michael J. Pazzani. J. Comb. Optim. (3): 301-320 (1999). Web SearchBibTeXDownload |
| 66 | Detecting Change in Categorical Data: Mining Contrast Sets. Stephen D. Bay, Michael J. Pazzani. KDD 1999, 302-306. Web SearchBibTeXDownload |
| 65 | A Principal Components Approach to Combining Regression Estimates. Christopher J. Merz, Michael J. Pazzani. Machine Learning (36): 9-32 (1999). Web SearchBibTeXDownload |
| 64 | Scaling up Dynamic Time Warping to Massive Dataset. Eamonn J. Keogh, Michael J. Pazzani. PKDD 1999, 1-11. Cited by 102Web SearchBibTeXDownload |
| 63 | Relevance Feedback Retrieval of Time Series Data. Eamonn J. Keogh, Michael J. Pazzani. SIGIR 1999, 183-190. Cited by 71Web SearchBibTeXDownload |
| 62 | Workshop on Recommender Systems: Algorithms and Evaluation. Ian Soboroff, Charles K. Nicholas, Michael J. Pazzani. SIGIR Forum (33): 36-43 (1999). Web SearchBibTeXDownload |
| 61 | An 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 |
|---|
| 60 | Knowledge-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 |
| 59 | Adjusted Probability Naive Bayesian Induction. Geoffrey I. Webb, Michael J. Pazzani. Australian Joint Conference on Artificial Intelligence 1998, 285-295. Web SearchBibTeXDownload |
| 58 | Learning with Globally Predictive Tests. Michael J. Pazzani. Discovery Science 1998, 220-231. Web SearchBibTeXDownload |
| 57 | Learning Collaborative Information Filters. Daniel Billsus, Michael J. Pazzani. ICML 1998, 46-54. Web SearchBibTeX |
| 56 | An 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 |
|---|
| 55 | Learning 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 |
| 54 | Detecting 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 |
| 53 | Knowledge Discovery from a Breast Cancer Database. Subramani Mani, Michael J. Pazzani, John West. AIME 1997, 130-133. Web SearchBibTeXDownload |
| 52 | Beyond Concise and Colorful: Learning Intelligible Rules. Michael J. Pazzani, Subramani Mani, William Rodman Shankle. KDD 1997, 235-238. Web SearchBibTeXDownload |
| 51 | On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Pedro Domingos, Michael J. Pazzani. Machine Learning (29): 103-130 (1997). Web SearchBibTeXDownload |
| 50 | Learning and Revising User Profiles: The Identification of Interesting Web Sites. Michael J. Pazzani, Daniel Billsus. Machine Learning (27): 313-331 (1997). Web SearchBibTeXDownload |
| 49 | The 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 |
|---|
| 48 | Syskill & Webert: Identifying Interesting Web Sites. Michael J. Pazzani, Jack Muramatsu, Daniel Billsus. AAAI/IAAI, Vol. 1 1996, 54-61. Web SearchBibTeXDownload |
| 47 | Simple Bayesian Classifiers Do Not Assume Independence. Pedro Domingos, Michael J. Pazzani. AAAI/IAAI, Vol. 2 1996, 1386. Web SearchBibTeXDownload |
| 46 | Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. Pedro Domingos, Michael J. Pazzani. ICML 1996, 105-112. Web SearchBibTeX |
| 45 | Tuning 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 |
| 44 | Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrac, Saso Dzeroski. Michael J. Pazzani. Machine Learning (23): 103-108 (1996). Web SearchBibTeXDownload |
| 43 | Review of ``Inductive Logic Programming: Techniques and Applications'' by Nada Lavrac, Saso Dzeroski. Michael J. Pazzani. Machine Learning (23): 103-108 (1996). Web SearchBibTeX |
| 42 | Error Reduction through Learning Multiple Descriptions. Kamal M. Ali, Michael J. Pazzani. Machine Learning (24): 173-202 (1996). Web SearchBibTeXDownload |
| 41 | Combining Neural Network Regression Estimates with Regularized Linear Weights. Christopher J. Merz, Michael J. Pazzani. NIPS 1996, 564-570. Web SearchBibTeXDownload |
| 1995 |
|---|
| 40 | A Lexical Based Semantic Bias for Theory Revision. Clifford Brunk, Michael J. Pazzani. ICML 1995, 81-89. Web SearchBibTeX |
| 39 | Learning Hierarchies from Ambiguous Natural Language Data. Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz. ICML 1995, 575-583. Web SearchBibTeX |
| 38 | An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers. Michael J. Pazzani. KDD 1995, 228-233. Web SearchBibTeXDownload |
| 37 | Acquiring 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 |
|---|
| 36 | Revision of Production System Rule-Bases. Patrick M. Murphy, Michael J. Pazzani. ICML 1994, 199-207. Web SearchBibTeX |
| 35 | Reducing Misclassification Costs. Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal Ali, Timothy Hume, Clifford Brunk. ICML 1994, 217-225. Web SearchBibTeX |
| 34 | On Learning Multiple Descriptions of a Concept. Kamal Ali, Clifford Brunk, Michael J. Pazzani. ICTAI 1994, 476-483. Web SearchBibTeX |
| 33 | Parameter Tuning for the MAX Expert System. Christopher J. Merz, Michael J. Pazzani. ICTAI 1994, 632-639. Web SearchBibTeX |
| 32 | Exploring 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 |
| 31 | Avoiding 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 |
| 30 | Guest Editor's Introduction. Sally A. McKee, Saumya K. Debray, Manuel V. Hermenegildo, Michael J. Maher. Machine Learning (16): 7-9 (1994). Web SearchBibTeXDownload |
| 1993 |
|---|
| 29 | Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. Michael J. Pazzani, Clifford Brunk. AAAI 1993, 328-334. Web SearchBibTeXDownload |
| 28 | A Methodology for Evaluating Theory Revision Systems: Results with Audrey II. James Wogulis, Michael J. Pazzani. IJCAI 1993, 1128-1134. Web SearchBibTeX |
| 27 | HYDRA: A Noise-tolerant Relational Concept Learning Algorithm. Kamal M. Ali, Michael J. Pazzani. IJCAI 1993, 1064-1071. Web SearchBibTeX |
| 26 | A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships. Michael J. Pazzani. Machine Learning (10): 185-190 (1993). Web SearchBibTeXDownload |
| 25 | Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning. Michael J. Pazzani. Machine Learning (11): 173-194 (1993). Web SearchBibTeXDownload |
| 1992 |
|---|
| 24 | The Utility of Knowledge in Inductive Learning. Michael J. Pazzani, Dennis F. Kibler. Machine Learning (9): 57-94 (1992). Web SearchBibTeXDownload |
| 23 | A Framework for Average Case Analysis of Conjunctive Learning Algorithms. Michael J. Pazzani, Wendy Sarrett. Machine Learning (9): 349-372 (1992). Web SearchBibTeXDownload |
| 22 | Average Case Analysis of Learning kappa-CNF Concepts. Daniel S. Hirschberg, Michael J. Pazzani. ML 1992, 206-211. Web SearchBibTeX |
| 1991 |
|---|
| 21 | A Computational Theory of Learning Causal Relationships. Michael J. Pazzani. Cognitive Science (15): 401-424 (1991). Web SearchBibTeXDownload |
| 20 | Constructive Induction of M-of-N Terms. Patrick M. Murphy, Michael J. Pazzani. ML 1991, 183-187. Web SearchBibTeX |
| 19 | An Investigation of Noise-Tolerant Relational Concept Learning Algorithms. Clifford Brunk, Michael J. Pazzani. ML 1991, 389-393. Web SearchBibTeX |
| 18 | Relational Clichés: Constraining Induction During Relational Learning. Glenn Silverstein, Michael J. Pazzani. ML 1991, 203-207. Web SearchBibTeX |
| 17 | A Knowledge-intensive Approach to Learning Relational Concepts. Michael J. Pazzani, Clifford Brunk, Glenn Silverstein. ML 1991, 432-436. Web SearchBibTeX |
| 1990 |
|---|
| 16 | Average Case Analysis of Conjunctive Learning Algorithms. Michael J. Pazzani, Wendy Sarrett. ML 1990, 339-347. Web SearchBibTeX |
| 1989 |
|---|
| 15 | Detecting and Correcting Errors of Omission After Explanation-Based Learning. Michael J. Pazzani. IJCAI 1989, 713-718. Web SearchBibTeX |
| 14 | One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning. Wendy Sarrett, Michael J. Pazzani. ML 1989, 26-28. Web SearchBibTeX |
| 13 | Explanation-Based Learning with Week Domain Theories. Michael J. Pazzani. ML 1989, 72-74. Web SearchBibTeX |
| 1988 |
|---|
| 12 | Integrating Explanation-Based and Empirical Learning Methods in OCCAM. Michael J. Pazzani. EWSL 1988, 147-165. Web SearchBibTeX |
| 11 | Integrated Learning with Incorrect and Incomplete Theories. Michael J. Pazzani. ML 1988, 291-297. Web SearchBibTeX |
| 1987 |
|---|
| 10 | A 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 |
| 9 | Using Prior Learning to Facilitate the Learning of New Causal Theories. Michael J. Pazzani, Michael G. Dyer, Margot Flowers. IJCAI 1987, 277-279. Web SearchBibTeX |
| 8 | Explanation-Based Learning for Knowledge-Based Systems. Michael J. Pazzani. International Journal of Man-Machine Studies (26): 413-433 (1987). Web SearchBibTeXDownload |
| 7 | Creating High Level Knowledge Structures from Simple Elements. Michael J. Pazzani. Knowledge Representation and Organization in Machine Learning 1987, 258-288. Web SearchBibTeXDownload |
| 1986 |
|---|
| 6 | Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning. Michael J. Pazzani. AAAI 1986, 1029-1035. Web SearchBibTeXDownload |
| 5 | The Role of Prior Causal Theories in Generalization. Michael J. Pazzani, Michael G. Dyer, Margot Flowers. AAAI 1986, 545-550. Web SearchBibTeXDownload |
| 1984 |
|---|
| 4 | Conceptual Analysis of Garden-Path Sentences. Michael J. Pazzani. COLING 1984, 486-490. Web SearchBibTeXDownload |
| 3 | Word-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 |
|---|
| 2 | Interactive Script Instantiation. Michael J. Pazzani. AAAI 1983, 320-326. Web SearchBibTeXDownload |
| 1 | Knowledge Based Question Answering. Michael J. Pazzani, Carl Engelman. ANLP 1983, 73-80. Web SearchBibTeXDownload |
