Michael J. Pazzani

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Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideEmail confirmado em cs.ucr.edu
Selina ChuResearch Scientist, Nasa Jet Propulsion Laboratory, California Institute of TechnologyEmail confirmado em jpl.nasa.gov
Chris MerzMastercardEmail confirmado em mastercard.com
Pedro DomingosProfessor of Computer Science and Engineering, University of WashingtonEmail confirmado em cs.washington.edu
Subramani ManiFormer faculty -- UNM-Albuquerque, Vanderbilt, UW-MilwaukeeEmail confirmado em salud.unm.edu
William ShankleUC Irvine Cognitive ScienceEmail confirmado em shankleclinic.com
Sharad MehrotraDistinguished Professor of Computer Science, Univ. of California, IrvineEmail confirmado em ics.uci.edu
Kamal AliSimplifaiEmail confirmado em simplifai.ai
Padhraic SmythDistinguished Professor, Computer Science, University of California IrvineEmail confirmado em ics.uci.edu
Douglas FisherVanderbilt UniversityEmail confirmado em vanderbilt.edu
Geoff WebbProfessor, Department of Data Science and Artificial Intelligence, Monash UniversityEmail confirmado em monash.edu
Mark AckermanUniversity of Michigan, Ann ArborEmail confirmado em umich.edu
Pat LangleyDirector, ISLE; Research Scientist, Stanford UniversityEmail confirmado em stanford.edu
Kamran AlipourApple Inc.Email confirmado em apple.com
Chelsea DobbinsThe University of QueenslandEmail confirmado em uq.edu.au
Matt JonesProfessor of Computer Science, Computational Foundry, Swansea UniversityEmail confirmado em swansea.ac.uk
George BuchananDeputy Dean of Research, School of Computing Technologies, RMIT UniversityEmail confirmado em rmit.edu.au
Harold ThimblebyProfessor of Computer Science, Swansea UniversityEmail confirmado em swansea.ac.uk
Gary MarsdenProfessor of Computer Science, University of Cape TownEmail confirmado em cs.uct.ac.za
Keli XiaoStony Brook UniversityEmail confirmado em stonybrook.edu

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Michael J. Pazzani

Distinguished Principal Scientist

Email confirmado em isi.edu - Página inicial

Machine Learning Explainable Artificial Intelligence Personalization Recommender Systems Search


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
On the optimality of the simple Bayesian classifier under zero-one loss P Domingos, M Pazzani Machine learning 29 (2-3), 103-130, 1997	4755	1997
Content-based recommendation systems MJ Pazzani, D Billsus The adaptive web: methods and strategies of web personalization, 325-341, 2007	4168	2007
A framework for collaborative, content-based and demographic filtering MJ Pazzani Artificial intelligence review 13, 393-408, 1999	2501	1999
Dimensionality reduction for fast similarity search in large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Knowledge and information Systems 3, 263-286, 2001	2256	2001
Learning and revising user profiles: The identification of interesting web sites M Pazzani, D Billsus Machine learning 27, 313-331, 1997	2069	1997
Learning collaborative information filters. D Billsus, MJ Pazzani Icml 98, 46-54, 1998	1931	1998
An online algorithm for segmenting time series E Keogh, S Chu, D Hart, M Pazzani Proceedings 2001 IEEE international conference on data mining, 289-296, 2001	1727	2001
Syskill & Webert: Identifying interesting web sites MJ Pazzani, J Muramatsu, D Billsus AAAI/IAAI, Vol. 1, 54-61, 1996	1263	1996
Locally adaptive dimensionality reduction for indexing large time series databases E Keogh, K Chakrabarti, M Pazzani, S Mehrotra Proceedings of the 2001 ACM SIGMOD international conference on Management of …, 2001	1243	2001
Beyond independence: Conditions for the optimality of the simple bayesian classi er P Domingos, M Pazzani Proc. 13th Intl. Conf. Machine Learning, 105-112, 1996	1230	1996
Scaling up dynamic time warping for datamining applications EJ Keogh, MJ Pazzani Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	1145	2000
Segmenting time series: A survey and novel approach E Keogh, S Chu, D Hart, M Pazzani Data mining in time series databases, 1-21, 2004	967	2004
User modeling for adaptive news access D Billsus, MJ Pazzani User modeling and user-adapted interaction 10, 147-180, 2000	936	2000
An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback. EJ Keogh, MJ Pazzani Kdd 98, 239-243, 1998	858	1998
A hybrid user model for news story classification D Billsus, MJ Pazzani UM99 User Modeling: Proceedings of the Seventh International Conference, 99-108, 1999	689	1999
Machine learning for user modeling GI Webb, MJ Pazzani, D Billsus User modeling and user-adapted interaction 11, 19-29, 2001	656	2001
Locally adaptive dimensionality reduction for indexing large time series databases K Chakrabarti, E Keogh, S Mehrotra, M Pazzani ACM Transactions on Database Systems (TODS) 27 (2), 188-228, 2002	579	2002
Detecting group differences: Mining contrast sets SD Bay, MJ Pazzani Data mining and knowledge discovery 5, 213-246, 2001	566	2001
Reducing misclassification costs M Pazzani, C Merz, P Murphy, K Ali, T Hume, C Brunk Machine Learning Proceedings 1994, 217-225, 1994	546	1994
The utility of knowledge in inductive learning M Pazzani, D Kibler Machine learning 9, 57-94, 1992	502	1992

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