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Michael J. Pazzani Vis

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*2007
101EEMichael J. Pazzani, Daniel Billsus: Content-Based Recommendation Systems. The Adaptive Web 2007: 325-341
100EEDaniel Billsus, Michael J. Pazzani: Adaptive News Access. The Adaptive Web 2007: 550-570
2006
99EESeth Hettich, Michael J. Pazzani: Mining for proposal reviewers: lessons learned at the national science foundation. KDD 2006: 862-871
2005
98EESerge 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
97EEMichael J. Pazzani: Machine Learning for Personalized Wireless Portals. ICTAI 2004: 3
2003
96EEMichael J. Pazzani: Adaptive Interfaces for Ubiquitous Web Access. User Modeling 2003: 1
95EESerge 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
94EEMichael J. Pazzani: Commercial Applications of Machine Learning for Personalized Wireless Portals. PRICAI 2002: 1-5
93EESelina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani: Iterative Deepening Dynamic Time Warping for Time Series. SDM 2002
92EEKaushik 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)
90EEDaniel Billsus, Clifford Brunk, Craig Evans, Brian Gladish, Michael J. Pazzani: Adaptive interfaces for ubiquitous web access. Commun. ACM 45(5): 34-38 (2002)
89EEEamonn J. Keogh, Michael J. Pazzani: Learning the Structure of Augmented Bayesian Classifiers. International Journal on Artificial Intelligence Tools 11(4): 587-601 (2002)
2001
88EEEamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani: An Online Algorithm for Segmenting Time Series. ICDM 2001: 289-296
87EEEamonn J. Keogh, Selina Chu, Michael J. Pazzani: Ensemble-index: a new approach to indexing large databases. KDD 2001: 117-125
86EEEamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. SIGMOD Conference 2001: 151-162
85EEGeorge 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)
83EEEamonn 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)
82EEGeoffrey 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
80EEMichael J. Pazzani: Representation of electronic mail filtering profiles: a user study. IUI 2000: 202-206
79EEDaniel Billsus, Michael J. Pazzani, James Chen: A learning agent for wireless news access. IUI 2000: 33-36
78EEEamonn 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
75EEMichael 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)
73EEStephen 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)
72EEDaniel Billsus, Michael J. Pazzani: User Modeling for Adaptive News Access. User Model. User-Adapt. Interact. 10(2-3): 147-180 (2000)
1999
71EESubramani 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
70EEDaniel Billsus, Michael J. Pazzani: A Personal News Agent That Talks, Learns and Explains. Agents 1999: 268-275
69EEMichael J. Pazzani, Daniel Billsus: Adaptive Web Site Agents. Agents 1999: 394-395
68EEStephen 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
66EEEamonn J. Keogh, Michael J. Pazzani: Relevance Feedback Retrieval of Time Series Data. SIGIR 1999: 183-190
65EEEamonn 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)
59EEIan 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
57EEGeoffrey I. Webb, Michael J. Pazzani: Adjusted Probability Naive Bayesian Induction. Australian Joint Conference on Artificial Intelligence 1998: 285-295
56EEMichael 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
43EEChristopher J. Merz, Michael J. Pazzani: Combining Neural Network Regression Estimates with Regularized Linear Weights. NIPS 1996: 564-570
42EEChristopher 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
3EEMichael J. Pazzani: Conceptual Analysis of Garden-Path Sentences. COLING 1984: 486-490
1983
2 Michael J. Pazzani: Interactive Script Instantiation. AAAI 1983: 320-326
1EEMichael J. Pazzani, Carl Engelman: Knowledge Based Question Answering. ANLP 1983: 73-80

Coauthor Index

1Serge Abiteboul [95] [98]
2Mark S. Ackerman [49] [50]
3Rakesh Agrawal [95] [98]
4Kamal Ali [33] [34]
5Kamal M. Ali [27] [40]
6Stephen D. Bay [68] [73] [81] [84]
7Philip A. Bernstein [95] [98]
8Daniel Billsus [46] [48] [49] [55] [69] [70] [72] [79] [82] [90] [91] [100] [101]
9Clifford Brunk [17] [18] [28] [31] [33] [34] [38] [90]
10George Buchanan [85]
11Michael J. Carey [95] [98]
12Stefano Ceri [95] [98]
13Kaushik Chakrabarti [83] [86] [92]
14James Chen [79]
15Selina Chu [87] [88] [93]
16Paul J. Cimoch [58] [64]
17W. Bruce Croft [95] [98]
18Andrea Pohoreckyj Danyluk [42]
19David J. DeWitt [95] [98]
20Sophia Deeds-Rubin [58] [64]
21Malcolm B. Dick [62] [71]
22Pedro Domingos [44] [45] [47]
23Michael G. Dyer [4] [8] [9]
24Carl Engelman [1]
25Floriana Esposito [31]
26Craig Evans [90]
27Sarah Farrant [85]
28Margot Flowers [4] [8]
29Michael J. Franklin (Mike Franklin) [95] [98]
30Scott Gaffney [49]
31Hector Garcia-Molina [95] [98]
32Dieter Gawlick [95] [98]
33Brian Gladish [90]
34Jim Gray [95] [98]
35Laura M. Haas [95] [98]
36Alon Y. Halevy (Alon Y. Levy) [95] [98]
37David Hart [88] [93]
38Joseph M. Hellerstein [95] [98]
39Seth Hettich [49] [99]
40Daniel S. Hirschberg [23]
41Timothy Hume [34]
42Yannis E. Ioannidis [95] [98]
43Matt Jones [85]
44Daniel Kempler [71]
45Eamonn J. Keogh [54] [65] [66] [67] [77] [78] [83] [86] [87] [88] [89] [92] [93]
46Martin L. Kersten [95] [98]
47Gordon Khoo [49]
48Dennis F. Kibler [21] [73]
49Dong Joon Kim [49]
50Raymond Klefstad [49]
51Richard H. Lathrop [58] [61] [64]
52Michael E. Lesk (Michael Lesk) [95] [98]
53Charles Lowe [49]
54Alexius Ludeman [49]
55David Maier [95] [98]
56Donato Malerba [31]
57Subramani Mani [51] [52] [53] [62] [71]
58Gary Marsden [85]
59Sharad Mehrotra [83] [86] [92]
60Christopher J. Merz [32] [34] [36] [39] [42] [43] [60]
61Koji Miyahara [76]
62Jack Muramatsu [46] [49]
63Patrick M. Murphy [20] [30] [34] [35]
64Jeffrey F. Naughton [95] [98]
65Charles K. Nicholas [59]
66Kazuo Omori [49]
67Miriam P. Raphael [58] [64]
68Wendy Sarrett [13] [15] [22]
69Hans-Jörg Schek [95] [98]
70Darryl M. See [58] [64]
71Timos K. Sellis [95] [98]
72Giovanni Semeraro [31]
73Douglas Semler [49]
74William Rodman Shankle [51] [52] [62]
75Abraham Silberschatz (Avi Silberschatz) [95] [98]
76Glenn Silverstein [17] [19]
77Padhraic Smyth [52] [73]
78Richard T. Snodgrass [95] [98]
79Ian Soboroff [59]
80Brian Starr [49] [50]
81Nicholas R. Steffen [58] [64]
82Michael Stonebraker [95] [98]
83I. Maribell Taussig [71]
84Evelyn L. Teng [71]
85Harold W. Thimbleby [85]
86Jeremiah G. Tilles [58] [64]
87Jeffrey D. Ullman [95] [98]
88Geoffrey I. Webb [57] [82]
89Gerhard Weikum [95] [98]
90John West [53]
91Jennifer Widom [95] [98]
92James Wogulis [26]
93Takefumi Yamazaki [36] [39]
94Paul Yap [49]
95Stanley B. Zdonik [95] [98]

Colors in the list of coauthors

Copyright © Tue Nov 3 08:52:44 2009 by Michael Ley (ley@uni-trier.de)