2003 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the development and understanding of machine learning algorithms and their relationship to human learning.
Michael J. Pazzani mainly investigates Artificial intelligence, Machine learning, World Wide Web, Data mining and Recommender system. Michael J. Pazzani works in the field of Artificial intelligence, focusing on Naive Bayes classifier in particular. His Machine learning research includes elements of Multi-task learning, Inductive bias and Domain theory.
His work on Web page and Web access as part of general World Wide Web research is frequently linked to Newspaper, bridging the gap between disciplines. Michael J. Pazzani has included themes like Representation and Cluster analysis in his Data mining study. Michael J. Pazzani interconnects Property and Knowledge extraction in the investigation of issues within Recommender system.
His primary areas of investigation include Artificial intelligence, Machine learning, World Wide Web, Naive Bayes classifier and Data mining. His research ties Natural language processing and Artificial intelligence together. His research integrates issues of Algorithm, Set and Bayesian probability in his study of Machine learning.
As part of his studies on World Wide Web, Michael J. Pazzani frequently links adjacent subjects like Information retrieval. His biological study spans a wide range of topics, including Classifier and Bayesian statistics. His Data mining study combines topics in areas such as Search engine indexing and Cluster analysis.
Michael J. Pazzani spends much of his time researching World Wide Web, Data mining, Artificial intelligence, Machine learning and Newspaper. His study in World Wide Web is interdisciplinary in nature, drawing from both Variety and User interface. The Data mining study combines topics in areas such as Wearable technology, Search engine indexing and Cluster analysis.
His Search engine indexing study incorporates themes from Time-series segmentation, Time series, Representation, Association rule learning and Euclidean distance. His study in the field of Bayesian probability, Conditional independence and Bayesian inference is also linked to topics like Ask price and Class. His work on Naive Bayes classifier, Stability and Online machine learning is typically connected to Proactive learning as part of general Machine learning study, connecting several disciplines of science.
His primary areas of investigation include World Wide Web, Series, Variety, Cluster analysis and Search engine indexing. Michael J. Pazzani has researched World Wide Web in several fields, including Context and User modeling. His Series research integrates issues from Algorithm, Dynamic time warping, Representation and Time series.
As part of one scientific family, Michael J. Pazzani deals mainly with the area of Variety, narrowing it down to issues related to the Web page, and often Recommender system. The concepts of his Search engine indexing study are interwoven with issues in Nearest neighbor search, Dimensionality reduction, Distance measures and Euclidean distance. The various areas that Michael J. Pazzani examines in his Data mining study include Property and Machine learning.
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On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Pedro Domingos;Michael Pazzani.
Machine Learning (1997)
Content-based recommendation systems
Michael J. Pazzani;Daniel Billsus.
The adaptive web (2007)
A Framework for Collaborative, Content-Based and Demographic Filtering
Michael J. Pazzani.
Artificial Intelligence Review (1999)
Learning and Revising User Profiles: The Identification ofInteresting Web Sites
Michael Pazzani;Daniel Billsus.
Machine Learning (1997)
Dimensionality reduction for fast similarity search in large time series databases
Eamonn J. Keogh;Kaushik Chakrabarti;Michael J. Pazzani;Sharad Mehrotra.
Knowledge and Information Systems (2001)
Learning Collaborative Information Filters
Daniel Billsus;Michael J. Pazzani.
international conference on machine learning (1998)
An online algorithm for segmenting time series
E. Keogh;S. Chu;D. Hart;M. Pazzani.
international conference on data mining (2001)
Syskill & webert: Identifying interesting web sites
Michael Pazzani;Jack Muramatsu;Daniel Billsus.
national conference on artificial intelligence (1996)
Derivative Dynamic Time Warping.
Eamonn J. Keogh;Michael J. Pazzani.
siam international conference on data mining (2001)
Locally adaptive dimensionality reduction for indexing large time series databases
Eamonn Keogh;Kaushik Chakrabarti;Michael Pazzani;Sharad Mehrotra.
international conference on management of data (2001)
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