- Ensemble-Index: A New Approach to Indexing Large Eamonn Keogh
- Segmenting Time Series: A Survey and Novel Approach Eamonn Keogh Selina Chu David Hart Michael Pazzani
- Relevance Feedback Retrieval of Time Series Data Eamonn J. Keogh
- Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases
- Adaptive Personalization for Mobile Content Delivery Daniel Billsus
- 34 May 2002/Vol. 45, No. 5 COMMUNICATIONS OF THE ACM The presentation of this information must
- Autonomous Agents and Multi-Agent Systems, 5, 205218, 2002 2002 Kluwer Academic Publishers. Manufactured in The Netherlands.
- Improvement of Collaborative Filtering with the Simple Bayesian Classifier
- The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation
- Acceptance by medical experts of rules generated by machine learning. M. J. Pazzani1
- Page 1 of 16 http://www10.org/cdrom/papers/230/index.html 2/11/03
- Characterizing Model Errors and Di erences Stephen D. Bay sbay@ics.uci.edu
- Collaborative Filtering with the Simple Bayesian Classifier Koji Miyahara1
- Learning Augmented Bayesian Classifiers: A Comparison of Distributionbased and Classificationbased Approaches
- A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases
- Iterative Deepening Dynamic Time Warping for Time Series
- Scaling up Dynamic Time Warping to Massive Datasets Eamonn J. Keogh and Michael J. Pazzani
- An Online Algorithm for Segmenting Time Series Eamonn Keogh Selina Chu David Hart Michael Pazzani
- An Improved Representation for Content-Based Television Personalization
- Commercial Applications of Machine Learning for Personalized Wireless Portals
- Derivative Dynamic Time Warping Eamonn J. Keogh
- Detecting Group Di erences: Mining Contrast Sets Stephen D. Bay and Michael J. Pazzani
- Characterizing Model Performance in the Feature Space Stephen D. Bay and Michael J. Pazzani
- Beyond Idiot Savants: Recommendations and Common Sense
- Scaling up Dynamic Time Warping to Massive Datasets Eamonn J. Keogh and Michael J. Pazzani
- User Modeling for Adaptive News Access DANIEL BILLSUS and MICHAEL J. PAZZANI
- Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases
- Explanation-based learning with weak domain theories Michael J. Pazzani (pazzani@ics.uci.edu)
- Mani et al. "Two-Stage Machine Learning Model for Guideline Development" 1 Two-Stage Machine Learning Model for Guideline Development
- Syskill & Webert: Identifying interesting web sites Michael Pazzani, Jack Muramatsu & Daniel Billsus
- Revising User Profiles: The Search for Interesting Web Sites Daniel Billsus and Michael Pazzani
- Indexing strategies for goal specific retrieval of cases Michael J. Pazzani (pazzani@ics.uci.edu)
- Selecting the Best Explanation for Explanation-Based Learning
- We discuss an approach to creating new terms during the induction of Horn clauses. The new terms enable
- Tuning Numeric Parameters of a Knowledge-Based System for Troubleshooting the Local Loop of the Telephone Network.
- Influence of Prior Knowledge on Concept Acquisition: Experimental and Computational Results
- Constructive Induction of Cartesian Product Michael J. Pazzani pazzani@ics.uci.edu
- HYDRA-MM: Learning Multiple Descriptions to Improve Classi cation Accuracy
- Revision of Production System Rule-Bases Patrick M. Murphy
- Knowledge-based Avoidance of Drug-Resistant HIV Mutants
- An iterative improvement approach for the discretization of numeric attributes in Bayesian classifiers
- Knowledge Acquisition with a Knowledge-Intensive Machine Learning System
- THEN modify the conjunction by adding the additional features that are indicative of the influence.
- Searching for dependencies in Bayesian classi ers Michael J. Pazzani
- Do I Care? --Tell Me What's Changed on the Web Brian Starr Mark S. Ackerman Michael Pazzani
- AUTOMATED REVISION OF CLIPS Patrick M. Murphy, pmurphy@ics.uci.edu
- Conceptual Analysis of Garden-Path Sentencee Michael J. Pamani
- ID2-of-3: Constructive Induction of M-of-N Concepts for Discriminators in Decision Trees
- Combining Neural Network Regression Estimates Using Principal Components
- Inducing Causal and Social Theories: A Prerequisite for Explanation-based Learning
- INTERACTIVE SCRIPT INSTANTIATION Michael J. Pazzani
- Automated Diagnosis of Attitude Control Anomalies Mkhaol J. Pazzanl and Anno F. Brlndlo
- We explore algorithms for learning classification procedures that attempt to minimize the cost of
- Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classi er
- Journal of Acquired Immune Dej'icicienc~Syadmmes and Human Retrovirology 15:356-362 0 1997 Lippincott-Raven Publishers, Philadelphia
- Acquiring and updating hierarchical knowledge for machine translation
- Machine Learning, 9, 57-94 (1992) 0 1992 Kluwer Academic Publishen. Boston. Manufactured in The Netherlands.
- Differential Diagnosis of Dementia: A Knowledge Discovery and Data Mining (KDD) Approach
- Syskill & Webert Page 1 of 10 http://www.ics.uci.edu/~pazzani/Syskill.html 5/18/98
- 10 Reducing the Small Disjuncts Problem by Learning Probabilistic Concept
- The Influence of Prior Theories on the Ease of Concept Acquisition
- We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of
- The Independent Sign Bias: Gaining Insight from Multiple Linear Regression Michael J. Pazzani (pazzani@ics.uci.edu)
- Comprehensible Knowledge Discovery: Gaining Insight from Data
- Machine Learning, 24, 173202 (1996) c 1996 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Improving Dementia Screening Tests with Machine Learning Methods
- We present an approach to modeling the average case behavior of an algorithm for learning
- Machine Learning 27, 313331 (1997) c 1997 Kluwer Academic Publishers. Manufactured in The Netherlands.
- We present an approach to modeling the average case behavior of learning algorithms. Our motivation is to predict the expected accuracy of learning algorithms as a function of the
- Journal of Combinatorial Optimization, 3, 301 320 1999 c 1999 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback
- Many learning algorithms form concept descriptions composed of clauses, each of which covers some
- Scientific Discovery in the Layperson Michael J. Pazzani
- Learning with Globally Predictive Tests Michael J. Pazzani
- ONE-SIDED ALGORITHMS FOR INTEGRATING EMPIRICAL AND EXPLANATION-BASED LEARNING
- Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning
- Combining Neural Network Regression Estimates with Regularized Linear
- A Framework for Collaborative, Content-Based and Demographic Filtering Michael J. Pazzani
- 1.0 Introduction There are two general approaches to learning
- The knowledge discovery and data mining (KDD) field draws on findings from statis-
- Journal of Arti cial Intelligence Research 1 (1994) 257-275 Submitted 11/93; published 3/94 Exploring the Decision Forest: An Empirical Investigation
- Machine Learning for User Modeling GEOFFREY I. WEBB1
- Knowledge Acquisifion (1991) 3, 157-173 Detecting and correcting errors in rule-based expert
- The Role of Prior Causal Theories in Generalization Michael Pazzani. Michael Dyer, Margot Flowers
- COGNITIVESCIENCE15, 401-424(1991) A Computational Theory
- Learning Augmented Bayesian Classifiers: A Comparison of Distribution-based and Classification-based Approaches
- Reply to "A Review of Creating a Memory of Causal Relationships" Michael Pazzani
- Relevance Feedback Retrieval of Time Series Data Eamonn J. Keogh
- An Investigation of Noise-Tolerant Relational Concept Learning Algorithms
- A Linguistically Based Semantic Bias for Theory Revision Clifford Brunk and Michael Pazzani
- Classi cation using Bayes Averaging of Multiple, Relational Rule-based Models
- IEKE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. PAMt.6, NO. 4, ,"LY ,984 493 Word-Meaning Selection in Multiprocess Language
- Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning
- Applying Machine Learning to an Alzheimer's Database
- Computational Models of Human Learning Michael Pazzani
- A Methodology for Evaluating Theory Revision Systems: Results with Audrey II
- Chapman, L. J., & Chapman, J. P. (1967). Genesis of popular but erroneous diagnostic observations. Journal of Abnormal Psychology, 72, 193-204.
- Exploring the Decision Forest Patrick M. Murphy & Michael J. Pazzani
- Learning Probabilistic User Models Daniel Billsus and Michael Pazzani
- To Appear: 1997 Cognitive Science Conference Comprehensible Knowledge-Discovery in Databases
- Detecting Change in Categorical Data: Mining Contrast Sets Stephen D. Bay and Michael J. Pazzani
- Handling Redundancy in Ensembles of Learned Models Using Principal Components
- Adaptive Web Site Agents Michael J. Pazzani and Daniel Billsus
- Acknowledgement We wish to thank Dr. Shigeo Kaneda, Dr. Satoru
- dica Page 1 of 4 http://www.ics.uci.edu/~pazzani/Publications/dica-chi96.html 5/18/98
- A Cluster Analysis Approach to Learning a Semantic Hierarchy for Machine Translation
- HYDRAMM: Learning Multiple Descriptions to Improve Classification Accuracy
- Finding Accurate Frontiers: A KnowledgeIntensive Approach to Relational Learning
- An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback
- A Methodology for Evaluating Theory Revision Systems: Results with Audrey II 3
- CTSHIV: A KnowledgeBased System For the Management of HIVinfected Patients
- Exploring the Decision Forest Patrick M. Murphy & Michael J. Pazzani
- A Cluster Analysis Approach to Learning a Semantic Hierarchy for Machine Translation
- ID2of3: Constructive Induction of M ofN Concepts for Discriminators in Decision Trees
- 100 K. ALI AND M. PAZZANI Error Reduction through Learning Multiple
- Learning with Globally Predictive Tests 1 Learning with Globally Predictive Tests
- An Indexing Scheme for Fast Similarity Search in Large Time Series Databases Eamonn J. Keogh and Michael J. Pazzani
- Syskill &Webert: Identifying interesting web sites Michael Pazzani, Jack Muramatsu & Daniel Billsus
- In Proc. Tenth Conf. on Innovative Applications of Artificial Intelligence, 1998, AAAI Press, Menlo Park, Calif. Innovative Application Award Winner.
- Combining Neural Network Regression Estimates with Regularized Linear
- Knowledge Acquisition with a KnowledgeIntensive Machine Learning System
- An investigation of noisetolerant relational concept learning algorithms
- Classification using Bayes Averaging of Multiple, Relational Rulebased Models
- , , 1--30 () fl Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Generating Models of Mental Retardation from Data with Machine Learning
- Handling Redundancy in Ensembles of Learned Models Using Principal Components
- Detecting Mental Retardation in Newborns and Infants: A Ma chine Learning Approach. Subramani Mani, MBBS, MS(Dept. of ICS, UCI)
- Combining Neural Network Regression Estimates Using Principal Components
- A Linguistically Based Semantic Bias for Theory Revision Clifford Brunk and Michael Pazzani
- Revision of Production System RuleBases Patrick M. Murphy
- On Learning Multiple Descriptions of a Kamal Ali, Clifford Brunk and Michael Pazzani
- Constructive Induction of Cartesian Product Michael J. Pazzani pazzani@ics.uci.edu
- Detecting Change in Categorical Data: Mining Contrast Sets Stephen D. Bay and Michael J. Pazzani
- Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier
- Application of a Genotypic Driven RuleBased Expert Artificial Intelligence Computer System in Treatment Experienced HIVInfected Patients.
- Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods
- Learning Probabilistic User Models Daniel Billsus and Michael Pazzani
- Dementia Screening with Machine Learning William R. Shankle 1 Subramani Mani 2
- Applying Machine Learning to an Alzheimer's Database 1
- Machine Learning, 0, 1--25 (1997) fl 1997 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- Searching for dependencies in Bayesian classifiers Michael J. Pazzani
- AHybrid User Model for News Story Classification Daniel Billsus and Michael J. Pazzani *
- Evaluating Adaptive Web Site Agents Michael J. Pazzani
- Journal of Artificial Intelligence Research 1 (1994) 257275 Submitted 11/93; published 3/94 Exploring the Decision Forest: An Empirical Investigation
- Improving Dementia Screening Tests with Machine Learning Methods
- Journal of Combinatorial Optimization, 3, 301--320 (1999) fl 1999 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands.
- We explore algorithms for learning classification procedures that attempt to minimize the cost of
- Knowledgebased Avoidance of DrugResistant HIV Mutants
- Many learning algorithms form concept descriptions composed of clauses, each of which covers some
- Revising User Profiles: The Search for Interesting Web Sites Daniel Billsus and Michael Pazzani
- Average Case Analysis of k-CNF and k-DNF learning algorithms
- In this paper, we address an issue that arises when the background knowledge used by explanation-
- CTSHIV: A Knowledge-Based System For the Management of HIV-infected Patients
- Detecting Mental Retardation in Newborns and Infants: A Ma-chine Learning Approach. Subramani Mani, MBBS, MS(Dept. of ICS, UCI)
- On Learning Multiple Descriptions of a Kamal Ali, Cliord Brunk and Michael Pazzani
- A Hybrid User Model for News Story Classification Daniel Billsus and Michael J. Pazzani*
- Learning Sets of Related Concepts: A Shared Task Model Tim Hume Michael J. Pazzani
- Discovering and Describing Category Differences: What makes a discovered difference insightful?
- Application of a Genotypic Driven Rule-Based Expert Artificial Intelligence Computer System in Treatment Experienced HIV-Infected Patients.
- Beyond Concise and Colorful: Learning Intelligible Rules Michael J. Pazzani
- 1. Introduction Machine learning algorithms that perform classification
- In Proc. Tenth Conf. on Innovative Applications of Arti cial Intelligence, 1998, AAAI Press, Menlo Park, Calif. Innovative Application Award Winner.
- An Indexing Scheme for Fast Similarity Search in Large Time Series Databases Eamonn J. Keogh and Michael J. Pazzani
- An Energy-Efficient Mobile Recommender System , Hui Xiong1
- P. Brusilovsky, A. Kobsa, and W. Nejdl (Eds.): The Adaptive Web, LNCS 4321, pp. 325 341, 2007. Springer-Verlag Berlin Heidelberg 2007
- Active Learning using On-line Algorithms Chris Mesterharm
