default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMichael J. Pazzani
- > Home > Persons > Michael J. Pazzani
Publications
- 2022
[i4]Kamran Alipour, Aditya Lahiri, Ehsan Adeli, Babak Salimi, Michael J. Pazzani:
Explaining Image Classifiers Using Contrastive Counterfactuals in Generative Latent Spaces. CoRR abs/2206.05257 (2022)- 2020
- [j40]Reza Rawassizadeh
, Hamidreza Keshavarz, Michael J. Pazzani:
Ghost Imputation: Accurately Reconstructing Missing Data of the Off Period. IEEE Trans. Knowl. Data Eng. 32(11): 2185-2197 (2020) - [c84]Amir Feghahati, Christian R. Shelton, Michael J. Pazzani, Kevin Tang:
CDeepEx: Contrastive Deep Explanations. ECAI 2020: 1143-1151 - 2019
- [j39]Reza Rawassizadeh, Taylan K. Sen, Sunny Jung Kim, Christian Meurisch, Hamidreza Keshavarz, Max Mühlhäuser, Michael J. Pazzani:
Manifestation of virtual assistants and robots into daily life: vision and challenges. CCF Trans. Pervasive Comput. Interact. 1(3): 163-174 (2019) - [j38]Reza Rawassizadeh, Chelsea Dobbins, Mohammad Akbari, Michael J. Pazzani:
Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering. Sensors 19(3): 448 (2019) - 2018
- [c83]Michael J. Pazzani, Amir Feghahati, Christian R. Shelton, Aaron R. Seitz:
Explaining Contrasting Categories. IUI Workshops 2018 - 2017
- [c82]Reza Rawassizadeh, Chelsea Dobbins, Manouchehr Nourizadeh, Zahra Ghamchili, Michael J. Pazzani:
A natural language query interface for searching personal information on smartwatches. PerCom Workshops 2017: 679-684 - 2016
- [j37]Reza Rawassizadeh, Elaheh Momeni, Chelsea Dobbins, Joobin Gharibshah, Michael J. Pazzani:
Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data. IEEE Trans. Knowl. Data Eng. 28(11): 3098-3112 (2016) - [i3]Reza Rawassizadeh, Chelsea Dobbins, Manouchehr Nourizadeh, Zahra Ghamchili, Michael J. Pazzani:
A Natural Language Query Interface for Searching Personal Information on Smartwatches. CoRR abs/1611.07139 (2016) - 2015
- [j36]Reza Rawassizadeh, Martin Tomitsch, Manouchehr Nourizadeh, Elaheh Momeni, Aaron Peery, Liudmila Ulanova, Michael J. Pazzani:
Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches. Sensors 15(9): 22616-22645 (2015) - 2010
- [c80]Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, Michael J. Pazzani:
An energy-efficient mobile recommender system. KDD 2010: 899-908 - 2007
- [c79]Michael J. Pazzani, Daniel Billsus:
Content-Based Recommendation Systems. The Adaptive Web 2007: 325-341 - [c78]Daniel Billsus, Michael J. Pazzani:
Adaptive News Access. The Adaptive Web 2007: 550-570 - 2006
- [c76]Seth Hettich, Michael J. Pazzani:
Mining for proposal reviewers: lessons learned at the national science foundation. KDD 2006: 862-871 - 2005
- [j35]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) - 2003
- [d3]Michael J. Pazzani, Amnon Meyers:
NSF Research Award Abstracts 1990-2003. UCI Machine Learning Repository, 2003
- [i2]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
- [j34]Michael J. Pazzani, Daniel Billsus:
Adaptive Web Site Agents. Auton. Agents Multi Agent Syst. 5(2): 205-218 (2002) - [j33]Daniel Billsus, Clifford Brunk, Craig Evans, Brian Gladish, Michael J. Pazzani:
Adaptive interfaces for ubiquitous web access. Commun. ACM 45(5): 34-38 (2002) - [j32]Eamonn J. Keogh, Michael J. Pazzani:
Learning the Structure of Augmented Bayesian Classifiers. Int. J. Artif. Intell. Tools 11(4): 587-601 (2002) - [j31]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) - [c72]Selina Chu, Eamonn J. Keogh, David M. Hart, Michael J. Pazzani:
Iterative Deepening Dynamic Time Warping for Time Series. SDM 2002: 195-212 - 2001
- [j30]Stephen D. Bay, Michael J. Pazzani:
Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl. Discov. 5(3): 213-246 (2001) - [j29]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) - [j28]Geoffrey I. Webb, Michael J. Pazzani, Daniel Billsus:
Machine Learning for User Modeling. User Model. User Adapt. Interact. 11(1-2): 19-29 (2001) - [c71]Eamonn J. Keogh, Selina Chu, David M. Hart, Michael J. Pazzani:
An Online Algorithm for Segmenting Time Series. ICDM 2001: 289-296 - [c70]Eamonn J. Keogh, Selina Chu, Michael J. Pazzani:
Ensemble-index: a new approach to indexing large databases. KDD 2001: 117-125 - [c69]Eamonn J. Keogh, Michael J. Pazzani:
Derivative Dynamic Time Warping. SDM 2001: 1-11 - [c68]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 - [c67]George Buchanan, Sarah Farrant, Matt Jones, Harold W. Thimbleby, Gary Marsden, Michael J. Pazzani:
Improving mobile internet usability. WWW 2001: 673-680 - 2000
- [j25]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 Explor. 2(2): 81-85 (2000) - [j24]Daniel Billsus, Michael J. Pazzani:
User Modeling for Adaptive News Access. User Model. User Adapt. Interact. 10(2-3): 147-180 (2000) - [c66]Stephen D. Bay, Michael J. Pazzani:
Characterizing Model Erros and Differences. ICML 2000: 49-56 - [c65]Daniel Billsus, Michael J. Pazzani, James Chen:
A learning agent for wireless news access. IUI 2000: 33-36 - [c63]Eamonn J. Keogh, Michael J. Pazzani:
Scaling up dynamic time warping for datamining applications. KDD 2000: 285-289 - [c62]Eamonn J. Keogh, Michael J. Pazzani:
A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. PAKDD 2000: 122-133 - 1999
- [j21]Subramani Mani, William Rodman Shankle, Malcolm B. Dick, Michael J. Pazzani:
Two-Stage Machine Learning model for guideline development. Artif. Intell. Medicine 16(1): 51-71 (1999) - [j19]Christopher J. Merz, Michael J. Pazzani:
A Principal Components Approach to Combining Regression Estimates. Mach. Learn. 36(1-2): 9-32 (1999) - [c60]Daniel Billsus, Michael J. Pazzani:
A Personal News Agent That Talks, Learns and Explains. Agents 1999: 268-275 - [c59]Michael J. Pazzani, Daniel Billsus:
Adaptive Web Site Agents. Agents 1999: 394-395 - [c58]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 - [c57]Eamonn J. Keogh, Michael J. Pazzani:
Learning augmented Bayesian classifiers: A comparison of distribution-based and classification-based approaches. AISTATS 1999 - [c56]Stephen D. Bay, Michael J. Pazzani:
Detecting Change in Categorical Data: Mining Contrast Sets. KDD 1999: 302-306 - [c55]Eamonn J. Keogh, Michael J. Pazzani:
Scaling up Dynamic Time Warping to Massive Dataset. PKDD 1999: 1-11 - [c54]Eamonn J. Keogh, Michael J. Pazzani:
Relevance Feedback Retrieval of Time Series Data. SIGIR 1999: 183-190 - [c53]Eamonn J. Keogh, Michael J. Pazzani:
An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. SSDBM 1999: 56-67 - [d2]Eamonn J. Keogh, Michael J. Pazzani:
Pseudo Periodic Synthetic Time Series. UCI Machine Learning Repository, 1999 - 1998
- [c51]Subramani Mani, Michael J. Pazzani:
Guideline generation from data by induction of decision tables using a Bayesian network framework. AMIA 1998 - [c50]Geoffrey I. Webb, Michael J. Pazzani:
Adjusted Probability Naive Bayesian Induction. Australian Joint Conference on Artificial Intelligence 1998: 285-295 - [c48]Daniel Billsus, Michael J. Pazzani:
Learning Collaborative Information Filters. ICML 1998: 46-54 - [c47]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 - [c46]William Rodman Shankle, Subramani Mani, Malcolm B. Dick, Michael J. Pazzani:
Simple Models for Estimating Dementia Severity Using Machine Learning. MedInfo 1998: 472-476 - 1997
- [j17]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 Mag. 18(2): 47-56 (1997) - [j16]Michael J. Pazzani, Daniel Billsus:
Learning and Revising User Profiles: The Identification of Interesting Web Sites. Mach. Learn. 27(3): 313-331 (1997) - [j15]Pedro M. Domingos, Michael J. Pazzani:
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Mach. Learn. 29(2-3): 103-130 (1997) - [c45]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 - [c44]Subramani Mani, Michael J. Pazzani, John West:
Knowledge Discovery from a Breast Cancer Database. AIME 1997: 130-133 - [c43]Christopher J. Merz, Michael J. Pazzani:
Combining Neural Network Regression Estimates Using Principal Components. AISTATS 1997: 363-370 - [c42]Subramani Mani, William Rodman Shankle, Michael J. Pazzani, Padhraic Smyth, Malcolm B. Dick:
Differential Diagnosis of Dementia: A Knowledge Discovery and Data Mining (KDD) Approach. AMIA 1997 - [c41]Michael J. Pazzani, Subramani Mani, William Rodman Shankle:
Beyond Concise and Colorful: Learning Intelligible Rules. KDD 1997: 235-238 - [c40]Mark S. Ackerman, Brian Starr, Michael J. Pazzani:
The Do-I-Care Agent: Effective Social Discovery and Filtering on the Web. RIAO 1997: 17-31 - 1996
- [j14]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) - [j12]Kamal M. Ali, Michael J. Pazzani:
Error Reduction through Learning Multiple Descriptions. Mach. Learn. 24(3): 173-202 (1996) - [c39]Michael J. Pazzani, Jack Muramatsu, Daniel Billsus:
Syskill & Webert: Identifying Interesting Web Sites. AAAI/IAAI, Vol. 1 1996: 54-61 - [c38]Pedro M. Domingos, Michael J. Pazzani:
Simple Bayesian Classifiers Do Not Assume Independence. AAAI/IAAI, Vol. 2 1996: 1386 - [c37]Brian Starr, Mark S. Ackerman, Michael J. Pazzani:
Do-I-Care: a collaborative Web agent. CHI Conference Companion 1996: 273-274 - [c36]Pedro M. Domingos, Michael J. Pazzani:
Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. ICML 1996: 105-112 - [c35]Christopher J. Merz, Michael J. Pazzani:
Combining Neural Network Regression Estimates with Regularized Linear Weights. NIPS 1996: 564-570 - 1995
- [j11]Kamal M. Ali, Michael J. Pazzani:
Hydra-mm: Learning Multiple Descriptions to Improve Classification Accuracy. Int. J. Artif. Intell. Tools 4(1-2): 115-134 (1995) - [c34]Kamal M. Ali, Michael J. Pazzani:
Classification Using Bayes Averaging of Multiple, Relational Rule-based Models. AISTATS 1995: 207-217 - [c32]Clifford Brunk, Michael J. Pazzani:
A Lexical Based Semantic Bias for Theory Revision. ICML 1995: 81-89 - [c31]Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz:
Learning Hierarchies from Ambiguous Natural Language Data. ICML 1995: 575-583 - [c29]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
- [j10]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. 1: 257-275 (1994) - [c27]Patrick M. Murphy, Michael J. Pazzani:
Revision of Production System Rule-Bases. ICML 1994: 199-207 - [c26]Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal M. Ali, Timothy Hume, Clifford Brunk:
Reducing Misclassification Costs. ICML 1994: 217-225 - [c25]Kamal M. Ali, Clifford Brunk, Michael J. Pazzani:
On Learning Multiple Descriptions of a Concept. ICTAI 1994: 476-483 - [c24]Christopher J. Merz, Michael J. Pazzani:
Parameter Tuning for the MAX Expert System. ICTAI 1994: 632-639 - [c23]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 - [i1]Patrick M. Murphy, Michael J. Pazzani:
Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction. CoRR abs/cs/9403101 (1994) - 1993
- [c22]Michael J. Pazzani, Clifford Brunk:
Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. AAAI 1993: 328-334 - [c21]Kamal M. Ali, Michael J. Pazzani:
HYDRA: A Noise-tolerant Relational Concept Learning Algorithm. IJCAI 1993: 1064-1071 - 1992
- [j6]Michael J. Pazzani, Dennis F. Kibler:
The Utility of Knowledge in Inductive Learning. Mach. Learn. 9: 57-94 (1992) - [c19]Daniel S. Hirschberg, Michael J. Pazzani:
Average Case Analysis of Learning kappa-CNF Concepts. ML 1992: 206-211 - 1991
- [c18]Patrick M. Murphy, Michael J. Pazzani:
Constructive Induction of M-of-N Terms. ML 1991: 183-187 - [c17]Glenn Silverstein, Michael J. Pazzani:
Relational Clichés: Constraining Induction During Relational Learning. ML 1991: 203-207 - [c16]Clifford Brunk, Michael J. Pazzani:
An Investigation of Noise-Tolerant Relational Concept Learning Algorithms. ML 1991: 389-393 - [c15]Michael J. Pazzani, Clifford Brunk, Glenn Silverstein:
A Knowledge-intensive Approach to Learning Relational Concepts. ML 1991: 432-436 - 1984
- [j1]Richard E. Cullingford, Michael J. Pazzani:
Word-Meaning Selection in Multiprocess Language Understanding Programs. IEEE Trans. Pattern Anal. Mach. Intell. 6(4): 493-509 (1984)
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-07-07 00:31 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
