| 2007 |
|---|
| 101 | EE | Michael J. Pazzani,
Daniel Billsus:
Content-Based Recommendation Systems.
The Adaptive Web 2007: 325-341 |
| 100 | EE | Daniel Billsus,
Michael J. Pazzani:
Adaptive News Access.
The Adaptive Web 2007: 550-570 |
| 2006 |
|---|
| 99 | EE | Seth Hettich,
Michael J. Pazzani:
Mining for proposal reviewers: lessons learned at the national science foundation.
KDD 2006: 862-871 |
| 2005 |
|---|
| 98 | EE | 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) |
| 2004 |
|---|
| 97 | EE | Michael J. Pazzani:
Machine Learning for Personalized Wireless Portals.
ICTAI 2004: 3 |
| 2003 |
|---|
| 96 | EE | Michael J. Pazzani:
Adaptive Interfaces for Ubiquitous Web Access.
User Modeling 2003: 1 |
| 95 | EE | 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) |
| 2002 |
|---|
| 94 | EE | Michael J. Pazzani:
Commercial Applications of Machine Learning for Personalized Wireless Portals.
PRICAI 2002: 1-5 |
| 93 | EE | Selina Chu,
Eamonn J. Keogh,
David Hart,
Michael J. Pazzani:
Iterative Deepening Dynamic Time Warping for Time Series.
SDM 2002 |
| 92 | EE | 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) |
| 91 | | Michael J. Pazzani,
Daniel Billsus:
Adaptive Web Site Agents.
Autonomous Agents and Multi-Agent Systems 5(2): 205-218 (2002) |
| 90 | EE | Daniel Billsus,
Clifford Brunk,
Craig Evans,
Brian Gladish,
Michael J. Pazzani:
Adaptive interfaces for ubiquitous web access.
Commun. ACM 45(5): 34-38 (2002) |
| 89 | EE | Eamonn J. Keogh,
Michael J. Pazzani:
Learning the Structure of Augmented Bayesian Classifiers.
International Journal on Artificial Intelligence Tools 11(4): 587-601 (2002) |
| 2001 |
|---|
| 88 | EE | Eamonn J. Keogh,
Selina Chu,
David Hart,
Michael J. Pazzani:
An Online Algorithm for Segmenting Time Series.
ICDM 2001: 289-296 |
| 87 | EE | Eamonn J. Keogh,
Selina Chu,
Michael J. Pazzani:
Ensemble-index: a new approach to indexing large databases.
KDD 2001: 117-125 |
| 86 | EE | 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 |
| 85 | EE | George Buchanan,
Sarah Farrant,
Matt Jones,
Harold W. Thimbleby,
Gary Marsden,
Michael J. Pazzani:
Improving mobile internet usability.
WWW 2001: 673-680 |
| 84 | | Stephen D. Bay,
Michael J. Pazzani:
Detecting Group Differences: Mining Contrast Sets.
Data Min. Knowl. Discov. 5(3): 213-246 (2001) |
| 83 | EE | 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) |
| 82 | EE | Geoffrey I. Webb,
Michael J. Pazzani,
Daniel Billsus:
Machine Learning for User Modeling.
User Model. User-Adapt. Interact. 11(1-2): 19-29 (2001) |
| 2000 |
|---|
| 81 | | Stephen D. Bay,
Michael J. Pazzani:
Characterizing Model Erros and Differences.
ICML 2000: 49-56 |
| 80 | EE | Michael J. Pazzani:
Representation of electronic mail filtering profiles: a user study.
IUI 2000: 202-206 |
| 79 | EE | Daniel Billsus,
Michael J. Pazzani,
James Chen:
A learning agent for wireless news access.
IUI 2000: 33-36 |
| 78 | EE | Eamonn J. Keogh,
Michael J. Pazzani:
Scaling up dynamic time warping for datamining applications.
KDD 2000: 285-289 |
| 77 | | Eamonn J. Keogh,
Michael J. Pazzani:
A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases.
PAKDD 2000: 122-133 |
| 76 | | Koji Miyahara,
Michael J. Pazzani:
Collaborative Filtering with the Simple Bayesian Classifier.
PRICAI 2000: 679-689 |
| 75 | EE | Michael J. Pazzani:
Knowledge discovery from data?
IEEE Intelligent Systems 15(2): 10-13 (2000) |
| 74 | | Michael J. Pazzani:
Learning with Globally Predictive Tests.
New Generation Comput. 18(1): 28-38 (2000) |
| 73 | EE | 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) |
| 72 | EE | Daniel Billsus,
Michael J. Pazzani:
User Modeling for Adaptive News Access.
User Model. User-Adapt. Interact. 10(2-3): 147-180 (2000) |
| 1999 |
|---|
| 71 | EE | 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 |
| 70 | EE | Daniel Billsus,
Michael J. Pazzani:
A Personal News Agent That Talks, Learns and Explains.
Agents 1999: 268-275 |
| 69 | EE | Michael J. Pazzani,
Daniel Billsus:
Adaptive Web Site Agents.
Agents 1999: 394-395 |
| 68 | EE | Stephen D. Bay,
Michael J. Pazzani:
Detecting Change in Categorical Data: Mining Contrast Sets.
KDD 1999: 302-306 |
| 67 | | Eamonn J. Keogh,
Michael J. Pazzani:
Scaling up Dynamic Time Warping to Massive Dataset.
PKDD 1999: 1-11 |
| 66 | EE | Eamonn J. Keogh,
Michael J. Pazzani:
Relevance Feedback Retrieval of Time Series Data.
SIGIR 1999: 183-190 |
| 65 | EE | Eamonn J. Keogh,
Michael J. Pazzani:
An Indexing Scheme for Fast Similarity Search in Large Time Series Databases.
SSDBM 1999: 56-67 |
| 64 | | 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) |
| 63 | | Michael J. Pazzani:
A Framework for Collaborative, Content-Based and Demographic Filtering.
Artif. Intell. Rev. 13(5-6): 393-408 (1999) |
| 62 | | 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) |
| 61 | | Richard H. Lathrop,
Michael J. Pazzani:
Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses.
J. Comb. Optim. 3(2-3): 301-320 (1999) |
| 60 | | Christopher J. Merz,
Michael J. Pazzani:
A Principal Components Approach to Combining Regression Estimates.
Machine Learning 36(1-2): 9-32 (1999) |
| 59 | EE | Ian Soboroff,
Charles K. Nicholas,
Michael J. Pazzani:
Workshop on Recommender Systems: Algorithms and Evaluation.
SIGIR Forum 33(1): 36-43 (1999) |
| 1998 |
|---|
| 58 | | 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 |
| 57 | | Geoffrey I. Webb,
Michael J. Pazzani:
Adjusted Probability Naive Bayesian Induction.
Australian Joint Conference on Artificial Intelligence 1998: 285-295 |
| 56 | EE | Michael J. Pazzani:
Learning with Globally Predictive Tests.
Discovery Science 1998: 220-231 |
| 55 | | Daniel Billsus,
Michael J. Pazzani:
Learning Collaborative Information Filters.
ICML 1998: 46-54 |
| 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 |
| 1997 |
|---|
| 53 | | Subramani Mani,
Michael J. Pazzani,
John West:
Knowledge Discovery from a Breast Cancer Database.
AIME 1997: 130-133 |
| 52 | | 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 |
| 51 | | Michael J. Pazzani,
Subramani Mani,
William Rodman Shankle:
Beyond Concise and Colorful: Learning Intelligible Rules.
KDD 1997: 235-238 |
| 50 | | 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 |
| 49 | | 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) |
| 48 | | Michael J. Pazzani,
Daniel Billsus:
Learning and Revising User Profiles: The Identification of Interesting Web Sites.
Machine Learning 27(3): 313-331 (1997) |
| 47 | | 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) |
| 1996 |
|---|
| 46 | | Michael J. Pazzani,
Jack Muramatsu,
Daniel Billsus:
Syskill & Webert: Identifying Interesting Web Sites.
AAAI/IAAI, Vol. 1 1996: 54-61 |
| 45 | | Pedro Domingos,
Michael J. Pazzani:
Simple Bayesian Classifiers Do Not Assume Independence.
AAAI/IAAI, Vol. 2 1996: 1386 |
| 44 | | Pedro Domingos,
Michael J. Pazzani:
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier.
ICML 1996: 105-112 |
| 43 | EE | Christopher J. Merz,
Michael J. Pazzani:
Combining Neural Network Regression Estimates with Regularized Linear Weights.
NIPS 1996: 564-570 |
| 42 | EE | 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) |
| 41 | | Michael J. Pazzani:
Review of ``Inductive Logic Programming: Techniques and Applications'' by Nada Lavrac, Saso Dzeroski.
Machine Learning 23(1): 103-108 (1996) |
| 40 | | Kamal M. Ali,
Michael J. Pazzani:
Error Reduction through Learning Multiple Descriptions.
Machine Learning 24(3): 173-202 (1996) |
| 1995 |
|---|
| 39 | | Takefumi Yamazaki,
Michael J. Pazzani,
Christopher J. Merz:
Learning Hierarchies from Ambiguous Natural Language Data.
ICML 1995: 575-583 |
| 38 | | Clifford Brunk,
Michael J. Pazzani:
A Lexical Based Semantic Bias for Theory Revision.
ICML 1995: 81-89 |
| 37 | | Michael J. Pazzani:
An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers.
KDD 1995: 228-233 |
| 36 | | 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 |
| 1994 |
|---|
| 35 | | Patrick M. Murphy,
Michael J. Pazzani:
Revision of Production System Rule-Bases.
ICML 1994: 199-207 |
| 34 | | Michael J. Pazzani,
Christopher J. Merz,
Patrick M. Murphy,
Kamal Ali,
Timothy Hume,
Clifford Brunk:
Reducing Misclassification Costs.
ICML 1994: 217-225 |
| 33 | | Kamal Ali,
Clifford Brunk,
Michael J. Pazzani:
On Learning Multiple Descriptions of a Concept.
ICTAI 1994: 476-483 |
| 32 | | Christopher J. Merz,
Michael J. Pazzani:
Parameter Tuning for the MAX Expert System.
ICTAI 1994: 632-639 |
| 31 | | 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 |
| 30 | | 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) |
| 29 | | Michael J. Pazzani:
Guest Editor's Introduction.
Machine Learning 16(1-2): 7-9 (1994) |
| 1993 |
|---|
| 28 | | Michael J. Pazzani,
Clifford Brunk:
Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning.
AAAI 1993: 328-334 |
| 27 | | Kamal M. Ali,
Michael J. Pazzani:
HYDRA: A Noise-tolerant Relational Concept Learning Algorithm.
IJCAI 1993: 1064-1071 |
| 26 | | James Wogulis,
Michael J. Pazzani:
A Methodology for Evaluating Theory Revision Systems: Results with Audrey II.
IJCAI 1993: 1128-1134 |
| 25 | | Michael J. Pazzani:
A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships.
Machine Learning 10: 185-190 (1993) |
| 24 | | Michael J. Pazzani:
Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning.
Machine Learning 11: 173-194 (1993) |
| 1992 |
|---|
| 23 | | Daniel S. Hirschberg,
Michael J. Pazzani:
Average Case Analysis of Learning kappa-CNF Concepts.
ML 1992: 206-211 |
| 22 | | Michael J. Pazzani,
Wendy Sarrett:
A Framework for Average Case Analysis of Conjunctive Learning Algorithms.
Machine Learning 9: 349-372 (1992) |
| 21 | | Michael J. Pazzani,
Dennis F. Kibler:
The Utility of Knowledge in Inductive Learning.
Machine Learning 9: 57-94 (1992) |
| 1991 |
|---|
| 20 | | Patrick M. Murphy,
Michael J. Pazzani:
Constructive Induction of M-of-N Terms.
ML 1991: 183-187 |
| 19 | | Glenn Silverstein,
Michael J. Pazzani:
Relational Clichés: Constraining Induction During Relational Learning.
ML 1991: 203-207 |
| 18 | | Clifford Brunk,
Michael J. Pazzani:
An Investigation of Noise-Tolerant Relational Concept Learning Algorithms.
ML 1991: 389-393 |
| 17 | | Michael J. Pazzani,
Clifford Brunk,
Glenn Silverstein:
A Knowledge-intensive Approach to Learning Relational Concepts.
ML 1991: 432-436 |
| 16 | | Michael J. Pazzani:
A Computational Theory of Learning Causal Relationships.
Cognitive Science 15(3): 401-424 (1991) |
| 1990 |
|---|
| 15 | | Michael J. Pazzani,
Wendy Sarrett:
Average Case Analysis of Conjunctive Learning Algorithms.
ML 1990: 339-347 |
| 1989 |
|---|
| 14 | | Michael J. Pazzani:
Detecting and Correcting Errors of Omission After Explanation-Based Learning.
IJCAI 1989: 713-718 |
| 13 | | Wendy Sarrett,
Michael J. Pazzani:
One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning.
ML 1989: 26-28 |
| 12 | | Michael J. Pazzani:
Explanation-Based Learning with Week Domain Theories.
ML 1989: 72-74 |
| 1988 |
|---|
| 11 | | Michael J. Pazzani:
Integrating Explanation-Based and Empirical Learning Methods in OCCAM.
EWSL 1988: 147-165 |
| 10 | | Michael J. Pazzani:
Integrated Learning with Incorrect and Incomplete Theories.
ML 1988: 291-297 |
| 1987 |
|---|
| 9 | | 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 |
| 8 | | Michael J. Pazzani,
Michael G. Dyer,
Margot Flowers:
Using Prior Learning to Facilitate the Learning of New Causal Theories.
IJCAI 1987: 277-279 |
| 7 | | Michael J. Pazzani:
Creating High Level Knowledge Structures from Simple Elements.
Knowledge Representation and Organization in Machine Learning 1987: 258-288 |
| 6 | | Michael J. Pazzani:
Explanation-Based Learning for Knowledge-Based Systems.
International Journal of Man-Machine Studies 26(4): 413-433 (1987) |
| 1986 |
|---|
| 5 | | Michael J. Pazzani:
Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning.
AAAI 1986: 1029-1035 |
| 4 | | Michael J. Pazzani,
Michael G. Dyer,
Margot Flowers:
The Role of Prior Causal Theories in Generalization.
AAAI 1986: 545-550 |
| 1984 |
|---|
| 3 | EE | Michael J. Pazzani:
Conceptual Analysis of Garden-Path Sentences.
COLING 1984: 486-490 |
| 1983 |
|---|
| 2 | | Michael J. Pazzani:
Interactive Script Instantiation.
AAAI 1983: 320-326 |
| 1 | EE | Michael J. Pazzani,
Carl Engelman:
Knowledge Based Question Answering.
ANLP 1983: 73-80 |
