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Note: Links lead to the DBLP on the Web. Michael J. Pazzani 77 Michael J. Pazzani, Daniel Billsus : Adaptive Web Site Agents. Autonomous Agents and Multi-Agent Systems 5 (2): 205-218 (2002) 76 Daniel Billsus , Clifford Brunk , Craig Evans , Brian Gladish , Michael J. Pazzani: Adaptive interfaces for ubiquitous web access. CACM 45 (5): 34-38 (2002) 75 Eamonn J. Keogh , Selina Chu , David Hart , Michael J. Pazzani: An Online Algorithm for Segmenting Time Series. ICDM 2001 : 289-296 74 Eamonn J. Keogh , Selina Chu , Michael J. Pazzani: Ensemble-index: a new approach to indexing large databases. KDD 2001 : 117-125 73 Eamonn J. Keogh , Kaushik Chakrabarti , Sharad Mehrotra , Michael J. Pazzani: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. SIGMOD Conference 2001 72 George Buchanan , Sarah Farrant , Matt Jones , Harold W. Thimbleby , Gary Marsden , Michael J. Pazzani: Improving mobile internet usability. WWW 2001 : 673-680 71 Stephen D. Bay , Michael J. Pazzani: Detecting Group Differences: Mining Contrast Sets. Data Mining and Knowledge Discovery 5 (3): 213-246 (2001) 70 Eamonn J. Keogh , Kaushik Chakrabarti , Michael J. Pazzani, Sharad Mehrotra : Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowledge and Information Systems 3 (3): 263-286 (2001) 69 Michael J. Pazzani: Representation of electronic mail filtering profiles: a user study. Intelligent User Interfaces 2000 : 202-206 68 Daniel Billsus , Michael J. Pazzani, James Chen : A learning agent for wireless news access. Intelligent User Interfaces 2000 : 33-36 67 Eamonn J. Keogh , Michael J. Pazzani: Scaling up dynamic time warping for datamining applications. KDD 2000 : 285-289 66 Eamonn J. Keogh , Michael J. Pazzani: A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. PAKDD 2000 : 122-133 65 Koji Miyahara , Michael J. Pazzani: Collaborative Filtering with the Simple Bayesian Classifier. PRICAI 2000 : 679-689 64 Michael J. Pazzani: Knowledge discovery from data? IEEE Intelligent Systems 15 (2): 10-13 (2000) 63 Michael J. Pazzani: Learning with Globally Predictive Tests. New Generation Computing 18 (1): 28-38 (2000) 62 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) 61 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 60 Daniel Billsus , Michael J. Pazzani: A Personal News Agent That Talks, Learns and Explains. Agents 1999 : 268-275 59 Michael J. Pazzani, Daniel Billsus : Adaptive Web Site Agents. Agents 1999 : 394-395 58 Stephen D. Bay , Michael J. Pazzani: Detecting Change in Categorical Data: Mining Contrast Sets. KDD 1999 : 302-306 57 Eamonn J. Keogh , Michael J. Pazzani: Scaling up Dynamic Time Warping to Massive Dataset. PKDD 1999 : 1-11 56 Eamonn J. Keogh , Michael J. Pazzani: Relevance Feedback Retrieval of Time Series Data. SIGIR 1999 : 183-190 55 Eamonn J. Keogh , Michael J. Pazzani: An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. SSDBM 1999 : 56-67 54 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) 53 Michael J. Pazzani: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review 13 (5-6): 393-408 (1999) 52 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) 51 Christopher J. Merz , Michael J. Pazzani: A Principal Components Approach to Combining Regression Estimates. Machine Learning 36 (1-2): 9-32 (1999) 50 Ian Soboroff , Charles K. Nicholas , Michael J. Pazzani: Workshop on Recommender Systems: Algorithms and Evaluation. SIGIR Forum 33 (1): 36-43 (1999) 49 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 48 Geoffrey I. Webb , Michael J. Pazzani: Adjusted Probability Naive Bayesian Induction. Australian Joint Conference on Artificial Intelligence 1998 : 285-295 47 Michael J. Pazzani: Learning with Globally Predictive Tests. Discovery Science 1998 : 220-231 46 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 45 Subramani Mani , Michael J. Pazzani, John West : Knowledge Discovery from a Breast Cancer Database. AIME 1997 : 130-133 44 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 43 Michael J. Pazzani, Subramani Mani , William Rodman Shankle : Beyond Concise and Colorful: Learning Intelligible Rules. KDD 1997 : 235-238 42 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) 41 Michael J. Pazzani, Daniel Billsus : Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27 (3): 313-331 (1997) 40 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) 39 Michael J. Pazzani, Jack Muramatsu , Daniel Billsus : Syskill & Webert: Identifying Interesting Web Sites. AAAI/IAAI, Vol. 1 1996 : 54-61 38 Pedro Domingos , Michael J. Pazzani: Simple Bayesian Classifiers Do Not Assume Independence. AAAI/IAAI, Vol. 2 1996 : 1386 37 Pedro Domingos , Michael J. Pazzani: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. ICML 1996 : 105-112 36 Michael J. Pazzani: Review of ``Inductive Logic Programming: Techniques and Applications'' by Nada Lavrac, Saso Dzeroski. Machine Learning 23 (1): 103-108 (1996) 35 Kamal M. Ali , Michael J. Pazzani: Error Reduction through Learning Multiple Descriptions. Machine Learning 24 (3): 173-202 (1996) 34 Takefumi Yamazaki , Michael J. Pazzani, Christopher J. Merz : Learning Hierarchies from Ambiguous Natural Language Data. ICML 1995 : 575-583 33 Clifford Brunk , Michael J. Pazzani: A Lexical Based Semantic Bias for Theory Revision. ICML 1995 : 81-89 32 Michael J. Pazzani: An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers. KDD 1995 : 228-233 31 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 30 Patrick M. Murphy , Michael J. Pazzani: Revision of Production System Rule-Bases. ICML 1994 : 199-207 29 Michael J. Pazzani, Christopher J. Merz , Patrick M. Murphy , Kamal Ali , Timothy Hume , Clifford Brunk : Reducing Misclassification Costs. ICML 1994 : 217-225 28 Kamal Ali , Clifford Brunk , Michael J. Pazzani: On Learning Multiple Descriptions of a Concept. ICTAI 1994 : 476-483 27 Christopher J. Merz , Michael J. Pazzani: Parameter Tuning for the MAX Expert System. ICTAI 1994 : 632-639 26 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 25 Patrick M. Murphy , Michael J. Pazzani: Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction. JAIR 1 : 257-275 (1994) 24 Michael J. Pazzani: Guest Editor's Introduction. Machine Learning 16 (1-2): 7-9 (1994) 23 Michael J. Pazzani, Clifford Brunk : Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. AAAI 1993 : 328-334 22 Kamal M. Ali , Michael J. Pazzani: HYDRA: A Noise-tolerant Relational Concept Learning Algorithm. IJCAI 1993 : 1064-1071 21 James Wogulis , Michael J. Pazzani: A Methodology for Evaluating Theory Revision Systems: Results with Audrey II. IJCAI 1993 : 1128-1134 20 Michael J. Pazzani: A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships. Machine Learning 10 : 185-190 (1993) 19 Michael J. Pazzani: Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning. Machine Learning 11 : 173-194 (1993) 18 Michael J. Pazzani, Wendy Sarrett : A Framework for Average Case Analysis of Conjunctive Learning Algorithms. Machine Learning 9 : 349-372 (1992) 17 Michael J. Pazzani, Dennis F. Kibler : The Utility of Knowledge in Inductive Learning. Machine Learning 9 : 57-94 (1992) 16 Patrick M. Murphy , Michael J. Pazzani: Constructive Induction of M-of-N Terms. ML 1991 : 183-187 15 Glenn Silverstein , Michael J. Pazzani: Relational Clichés: Constraining Induction During Relational Learning. ML 1991 : 203-207 14 Clifford Brunk , Michael J. Pazzani: An Investigation of Noise-Tolerant Relational Concept Learning Algorithms. ML 1991 : 389-393 13 Michael J. Pazzani, Clifford Brunk , Glenn Silverstein : A Knowledge-intensive Approach to Learning Relational Concepts. ML 1991 : 432-436 12 Michael J. Pazzani: A Computational Theory of Learning Causal Relationships. Cognitive Science 15 (3): 401-424 (1991) 11 Michael J. Pazzani, Wendy Sarrett : Average Case Analysis of Conjunctive Learning Algorithms. ML 1990 : 339-347 10 Michael J. Pazzani: Detecting and Correcting Errors of Omission After Explanation-Based Learning. IJCAI 1989 : 713-718 9 Michael J. Pazzani: Integrating Explanation-Based and Empirical Learning Methods in OCCAM. EWSL 1988 : 147-165 8 Michael J. Pazzani: Integrated Learning with Incorrect and Incomplete Theories. ML 1988 : 291-297 7 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 6 Michael J. Pazzani, Michael G. Dyer , Margot Flowers : Using Prior Learning to Facilitate the Learning of New Causal Theories. IJCAI 1987 : 277-279 5 Michael J. Pazzani: Creating High Level Knowledge Structures from Simple Elements. Knowledge Representation and Organization in Machine Learning 1987 : 258-288 4 Michael J. Pazzani: Explanation-Based Learning for Knowledge-Based Systems. International Journal of Man-Machine Studies 26 (4): 413-433 (1987) 3 Michael J. Pazzani: Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning. AAAI 1986 : 1029-1035 2 Michael J. Pazzani, Michael G. Dyer , Margot Flowers : The Role of Prior Causal Theories in Generalization. AAAI 1986 : 545-550 1 Michael J. Pazzani: Interactive Script Instantiation. AAAI 1983 : 320-326  DiSC'02 © 2003 Association for Computing Machinery |
