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Michael J. Pazzani
Person information
- affiliation: University of California, San Diego, USA
- affiliation (former): University of California, Riverside, USA
- affiliation (former): University of California, Irvine, USA
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2020 – today
- 2023
- [j44]Kyle Hasenstab
, Justin Huynh
Feature Interpretation Using Generative Adversarial Networks (FIGAN): A Framework for Visualizing a CNN's Learned Features. IEEE Access 11: 5144-5160 (2023) - 2022
- [j43]Justin Huynh
Deep Learning Radiographic Assessment of Pulmonary Edema: Optimizing Clinical Performance, Training With Serum Biomarkers. IEEE Access 10: 48577-48588 (2022) - [c87]Michael J. Pazzani, Severine Soltani, Robert A. Kaufman, Samson Qian, Albert Hsiao:
Expert-Informed, User-Centric Explanations for Machine Learning. AAAI 2022: 12280-12286 - [c86]Severine Soltani, Robert A. Kaufman, Michael J. Pazzani:
User-Centric Enhancements to Explainable AI Algorithms for Image Classification. CogSci 2022 - [c85]Justin Huynh, Samira Masoudi, Abraham Noorbakhsh, Kyle Hasenstab, Michael J. Pazzani, Albert Hsiao:
Deep Learning Radiographic Assessment of Pulmonary Edema: Training with Serum Biomarkers. MIDL 2022: 590-604 - [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) - 2021
- [j42]Praharsh Ivaturi, Matteo Gadaleta
A Comprehensive Explanation Framework for Biomedical Time Series Classification. IEEE J. Biomed. Health Informatics 25(7): 2398-2408 (2021) - 2020
- [j41]Reza Rawassizadeh
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
2010 – 2019
- 2019
- [j40]Reza Rawassizadeh
Manifestation of virtual assistants and robots into daily life: vision and challenges. CCF Trans. Pervasive Comput. Interact. 1(3): 163-174 (2019) - [j39]Reza Rawassizadeh
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
A natural language query interface for searching personal information on smartwatches. PerCom Workshops 2017: 679-684 - 2016
- [j38]Reza Rawassizadeh
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
- [j37]Reza Rawassizadeh
Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches. Sensors 15(9): 22616-22645 (2015) - 2011
- [c81]Chris Mesterharm, Michael J. Pazzani:
Active learning using on-line algorithms. KDD 2011: 850-858 - [e1]Pearl Pu, Michael J. Pazzani, Elisabeth André
Proceedings of the 16th International Conference on Intelligent User Interfaces, IUI 2011, Palo Alto, CA, USA, February 13-16, 2011. ACM 2011, ISBN 978-1-4503-0419-1 [contents] - [d4]Chris Mesterharm, Michael J. Pazzani:
Farm Ads. UCI Machine Learning Repository, 2011 - 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
2000 – 2009
- 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
- [c77]Michael J. Pazzani:
Adaptive Info - Personalizing the Wireless Web: Machine Learning 275 and Business 101. AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications 2006: 12 - [c76]Seth Hettich, Michael J. Pazzani:
Mining for proposal reviewers: lessons learned at the national science foundation. KDD 2006: 862-871 - 2005
- [j36]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
The Lowell database research self-assessment. Commun. ACM 48(5): 111-118 (2005) - 2004
- [c75]Michael J. Pazzani:
Machine Learning for Personalized Wireless Portals. ICTAI 2004: 3 - 2003
- [c74]Michael J. Pazzani:
Adaptive Interfaces for Ubiquitous Web Access. User Modeling 2003: 1 - [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
- [j35]Michael J. Pazzani, Daniel Billsus:
Adaptive Web Site Agents. Auton. Agents Multi Agent Syst. 5(2): 205-218 (2002) - [j34]Daniel Billsus, Clifford Brunk, Craig Evans, Brian Gladish, Michael J. Pazzani:
Adaptive interfaces for ubiquitous web access. Commun. ACM 45(5): 34-38 (2002) - [j33]Eamonn J. Keogh, Michael J. Pazzani:
Learning the Structure of Augmented Bayesian Classifiers. Int. J. Artif. Intell. Tools 11(4): 587-601 (2002) - [j32]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) - [c73]Michael J. Pazzani:
Commercial Applications of Machine Learning for Personalized Wireless Portals. PRICAI 2002: 1-5 - [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
- [j31]Stephen D. Bay, Michael J. Pazzani:
Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl. Discov. 5(3): 213-246 (2001) - [j30]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) - [j29]Geoffrey I. Webb
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
Improving mobile internet usability. WWW 2001: 673-680 - 2000
- [j28]Michael J. Pazzani:
Knowledge discovery from data? IEEE Intell. Syst. 15(2): 10-13 (2000) - [j27]Michael J. Pazzani:
Learning with Globally Predictive Tests. New Gener. Comput. 18(1): 28-38 (2000) - [j26]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) - [j25]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 - [c64]Michael J. Pazzani:
Representation of electronic mail filtering profiles: a user study. IUI 2000: 202-206 - [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 - [c61]Koji Miyahara, Michael J. Pazzani:
Collaborative Filtering with the Simple Bayesian Classifier. PRICAI 2000: 679-689
1990 – 1999
- 1999
- [j24]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 Mag. 20(1): 13-25 (1999) - [j23]Michael J. Pazzani:
A Framework for Collaborative, Content-Based and Demographic Filtering. Artif. Intell. Rev. 13(5-6): 393-408 (1999) - [j22]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) - [j21]Richard H. Lathrop, Michael J. Pazzani:
Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses. J. Comb. Optim. 3(2-3): 301-320 (1999) - [j20]Christopher J. Merz, Michael J. Pazzani:
A Principal Components Approach to Combining Regression Estimates. Mach. Learn. 36(1-2): 9-32 (1999) - [j19]Ian Soboroff, Charles K. Nicholas, Michael J. Pazzani:
Workshop on Recommender Systems: Algorithms and Evaluation. SIGIR Forum 33(1): 36-43 (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
- [c52]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 - [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 - [c49]Michael J. Pazzani:
Learning with Globally Predictive Tests. Discovery Science 1998: 220-231 - [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 - [d1]Michael J. Pazzani:
Syskill and Webert Web Page Ratings. UCI Machine Learning Repository, 1998 - 1997
- [j18]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) - [j17]Michael J. Pazzani, Daniel Billsus:
Learning and Revising User Profiles: The Identification of Interesting Web Sites. Mach. Learn. 27(3): 313-331 (1997) - [j16]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
- [j15]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) - [j14]Michael J. Pazzani:
Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrac, Saso Dzeroski. Mach. Learn. 23(1): 103-108 (1996) - [j13]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
- [j12]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 - [c33]Michael J. Pazzani:
Searching for Dependencies in Bayesian Classifiers. AISTATS 1995: 239-248 - [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 - [c30]Michael J. Pazzani, Larry Nguyen, Stefanus Mantik:
Learning from hotlists and coldlists: towards a WWW information filtering and seeking agent. ICTAI 1995: 492-495 - [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 - [c28]Michael J. Pazzani:
An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers. KDD 1995: 228-233 - 1994
- [j11]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) - [j10]Michael J. Pazzani:
Guest Editor's Introduction. Mach. Learn. 16(1-2): 7-9 (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
- [j9]Michael J. Pazzani:
A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships. Mach. Learn. 10: 185-190 (1993) - [j8]Michael J. Pazzani:
Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning. Mach. Learn. 11: 173-194 (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 - [c20]James Wogulis, Michael J. Pazzani:
A Methodology for Evaluating Theory Revision Systems: Results with Audrey II. IJCAI 1993: 1128-1134 - 1992
- [j7]Michael J. Pazzani, Dennis F. Kibler:
The Utility of Knowledge in Inductive Learning. Mach. Learn. 9: 57-94 (1992) - [j6]Michael J. Pazzani, Wendy Sarrett:
A Framework for Average Case Analysis of Conjunctive Learning Algorithms. Mach. Learn. 9: 349-372 (1992) - [c19]Daniel S. Hirschberg, Michael J. Pazzani:
Average Case Analysis of Learning kappa-CNF Concepts. ML 1992: 206-211 - 1991
- [j5]Michael J. Pazzani:
A Computational Theory of Learning Causal Relationships. Cogn. Sci. 15(3): 401-424 (1991) - [j4]Michael J. Pazzani, Clifford Brunk:
Detecting and correcting errors in rule-based expert systems: an integration of empirical and explanation-based learning. Knowl. Acquis. 3(2): 157-173 (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 - 1990
- [b1]Michael J. Pazzani:
Creating a memory of causal relationships - an integration of empirical and explanation-based learning methods. Lawrence Erlbaum 1990, pp. 1-350 - [c14]Michael J. Pazzani, Wendy Sarrett:
Average Case Analysis of Conjunctive Learning Algorithms. ML 1990: 339-347
1980 – 1989
- 1989
- [c13]Wendy Sarrett, Michael J. Pazzani:
One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning. ML 1989: 26-28 - [c12]Michael J. Pazzani:
Explanation-Based Learning with Week Domain Theories. ML 1989: 72-74 - [c11]Michael J. Pazzani:
Detecting and Correcting Errors of Omission After Explanation-Based Learning. IJCAI 1989: 713-718 - 1988
- [c10]Michael J. Pazzani:
Integrating Explanation-Based and Empirical Learning Methods in OCCAM. EWSL 1988: 147-165 - [c9]Michael J. Pazzani:
Integrated Learning with Incorrect and Incomplete Theories. ML 1988: 291-297 - 1987
- [j3]Michael J. Pazzani:
Explanation-Based Learning for Knowledge-Based Systems. Int. J. Man Mach. Stud. 26(4): 413-433 (1987) - [j2]Michael J. Pazzani:
Failure-Driven Learning of Fault Diagnosis Heuristics. IEEE Trans. Syst. Man Cybern. 17(3): 380-394 (1987) - [c8]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 - [c7]Michael J. Pazzani, Michael G. Dyer, Margot Flowers:
Using Prior Learning to Facilitate the Learning of New Causal Theories. IJCAI 1987: 277-279 - [c6]Michael J. Pazzani:
Creating High Level Knowledge Structures from Simple Elements. Knowledge Representation and Organization in Machine Learning 1987: 258-288 - 1986
- [c5]Michael J. Pazzani, Michael G. Dyer, Margot Flowers:
The Role of Prior Causal Theories in Generalization. AAAI 1986: 545-550 - [c4]Michael J. Pazzani:
Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning. AAAI 1986: 1029-1035 - 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) - [c3]Michael J. Pazzani:
Conceptual Analysis of Garden-Path Sentences. COLING 1984: 486-490 - 1983
- [c2]Michael J. Pazzani:
Interactive Script Instantiation. AAAI 1983: 320-326 - [c1]Michael J. Pazzani, Carl Engelman:
Knowledge Based Question Answering. ANLP 1983: 73-80
Coauthor Index
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