Pazzani, Michael J.

44 publications

AAAI 2022 Expert-Informed, User-Centric Explanations for Machine Learning Michael J. Pazzani, Severine Soltani, Robert Kaufman, Samson Qian, Albert Hsiao

ICML 2000 Characterizing Model Erros and Differences Stephen D. Bay, Michael J. Pazzani

MLJ 1999 A Principal Components Approach to Combining Regression Estimates Christopher J. Merz, Michael J. Pazzani

AAAI 1998 Knowledge-Based Avoidance of Drug-Resistant HIV Mutants Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles

ICML 1998 Learning Collaborative Information Filters Daniel Billsus, Michael J. Pazzani

AISTATS 1997 Combining Neural Network Regression Estimates Using Principal Components Christopher J. Merz, Michael J. Pazzani

MLJ 1997 Learning and Revising User Profiles: The Identification of Interesting Web Sites Michael J. Pazzani, Daniel Billsus

MLJ 1997 On the Optimality of the Simple Bayesian Classifier Under Zero-One Loss Pedro M. Domingos, Michael J. Pazzani

ICML 1996 Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier Pedro M. Domingos, Michael J. Pazzani

NeurIPS 1996 Combining Neural Network Regression Estimates with Regularized Linear Weights Christopher J. Merz, Michael J. Pazzani

MLJ 1996 Error Reduction Through Learning Multiple Descriptions Kamal M. Ali, Michael J. Pazzani

MLJ 1996 Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrac, Saso Dzeroski Michael J. Pazzani

AAAI 1996 Simple Bayesian Classifiers Do Not Assume Independence Pedro M. Domingos, Michael J. Pazzani

AAAI 1996 Syskill & Webert: Identifying Interesting Web Sites Michael J. Pazzani, Jack Muramatsu, Daniel Billsus

ICML 1995 A Lexical Based Semantic Bias for Theory Revision Clifford Brunk, Michael J. Pazzani

ICML 1995 Learning Hierarchies from Ambiguous Natural Language Data Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz

AISTATS 1995 Learning Multiple Relational Rule-Based Models Kamal M. Ali, Clifford Brunk, Michael J. Pazzani

AISTATS 1995 Searching for Dependencies in Bayesian Classifiers Michael J. Pazzani

JAIR 1994 Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction Patrick M. Murphy, Michael J. Pazzani

MLJ 1994 Guest Editor's Introduction Michael J. Pazzani

ICML 1994 Reducing Misclassification Costs Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal M. Ali, Timothy Hume, Clifford Brunk

ICML 1994 Revision of Production System Rule-Bases Patrick M. Murphy, Michael J. Pazzani

IJCAI 1993 A Methodology for Evaluating Theory Revision Systems: Results with Audrey II James Wogulis, Michael J. Pazzani

MLJ 1993 A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships Michael J. Pazzani

AAAI 1993 Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning Michael J. Pazzani, Clifford Brunk

IJCAI 1993 HYDRA: A Noise-Tolerant Relational Concept Learning Algorithm Kamal M. Ali, Michael J. Pazzani

MLJ 1993 Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning Michael J. Pazzani

MLJ 1992 A Framework for Average Case Analysis of Conjunctive Learning Algorithms Michael J. Pazzani, Wendy Sarrett

ICML 1992 Average Case Analysis of Learning Kappa-CNF Concepts Daniel S. Hirschberg, Michael J. Pazzani

MLJ 1992 The Utility of Knowledge in Inductive Learning Michael J. Pazzani, Dennis F. Kibler

ICML 1991 A Knowledge-Intensive Approach to Learning Relational Concepts Michael J. Pazzani, Clifford Brunk, Glenn Silverstein

ICML 1991 An Investigation of Noise-Tolerant Relational Concept Learning Algorithms Clifford Brunk, Michael J. Pazzani

ICML 1991 Constructive Induction of M-of-N Terms Patrick M. Murphy, Michael J. Pazzani

ICML 1991 Relational Clichés: Constraining Induction During Relational Learning Glenn Silverstein, Michael J. Pazzani

ICML 1990 Average Case Analysis of Conjunctive Learning Algorithms Michael J. Pazzani, Wendy Sarrett

IJCAI 1989 Detecting and Correcting Errors of Omission After Explanation-Based Learning Michael J. Pazzani

ICML 1989 Explanation-Based Learning with Week Domain Theories Michael J. Pazzani

ICML 1989 One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning Wendy Sarrett, Michael J. Pazzani

ICML 1988 Integrated Learning with Incorrect and Incomplete Theories Michael J. Pazzani

IJCAI 1987 A Comparison of Concept Identification in Human Learning and Network Learning with the Generalized Delta Rule Michael J. Pazzani, Michael G. Dyer

IJCAI 1987 Using Prior Learning to Facilitate the Learning of New Causal Theories Michael J. Pazzani, Michael G. Dyer, Margot Flowers

AAAI 1986 Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning Michael J. Pazzani

AAAI 1986 The Role of Prior Causal Theories in Generalization Michael J. Pazzani, Michael G. Dyer, Margot Flowers

AAAI 1983 Interactive Script Instantiation Michael J. Pazzani