‪Michael J. Pazzani‬ -巨人学术搜索‬‬

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Eamonn KeoghDistinguished Professor of Computer Science, University of California - Riverside在 cs.ucr.edu 的电子邮件经过验证
Selina ChuResearch Scientist, Nasa Jet Propulsion Laboratory, California Institute of Technology在 jpl.nasa.gov 的电子邮件经过验证
Chris MerzMastercard在 mastercard.com 的电子邮件经过验证
Pedro DomingosProfessor of Computer Science and Engineering, University of Washington在 cs.washington.edu 的电子邮件经过验证
Subramani ManiFormer faculty -- UNM-Albuquerque, Vanderbilt, UW-Milwaukee在 salud.unm.edu 的电子邮件经过验证
William ShankleUC Irvine Cognitive Science在 shankleclinic.com 的电子邮件经过验证
Sharad MehrotraDistinguished Professor of Computer Science, Univ. of California, Irvine在 ics.uci.edu 的电子邮件经过验证
Kamal AliSimplifai在 simplifai.ai 的电子邮件经过验证
Padhraic SmythDistinguished Professor, Computer Science, University of California Irvine在 ics.uci.edu 的电子邮件经过验证
Douglas FisherVanderbilt University在 vanderbilt.edu 的电子邮件经过验证
Geoff WebbProfessor, Department of Data Science and Artificial Intelligence, Monash University在 monash.edu 的电子邮件经过验证
Mark AckermanUniversity of Michigan, Ann Arbor在 umich.edu 的电子邮件经过验证
Pat LangleyDirector, ISLE; Research Scientist, Stanford University在 stanford.edu 的电子邮件经过验证
Kamran AlipourApple Inc.在 apple.com 的电子邮件经过验证
Chelsea DobbinsThe University of Queensland在 uq.edu.au 的电子邮件经过验证
Matt JonesProfessor of Computer Science, Computational Foundry, Swansea University在 swansea.ac.uk 的电子邮件经过验证
George BuchananDeputy Dean of Research, School of Computing Technologies, RMIT University在 rmit.edu.au 的电子邮件经过验证
Harold ThimblebyProfessor of Computer Science, Swansea University在 swansea.ac.uk 的电子邮件经过验证
Gary MarsdenProfessor of Computer Science, University of Cape Town在 cs.uct.ac.za 的电子邮件经过验证
Keli XiaoStony Brook University在 stonybrook.edu 的电子邮件经过验证

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Michael J. Pazzani

Distinguished Principal Scientist

在 isi.edu 的电子邮件经过验证 - 首页

Machine Learning Explainable Artificial Intelligence Personalization Recommender Systems Search


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
On the optimality of the simple Bayesian classifier under zero-one loss P Domingos, M Pazzani Machine learning 29 (2-3), 103-130, 1997	4756	1997
Content-based recommendation systems MJ Pazzani, D Billsus The adaptive web: methods and strategies of web personalization, 325-341, 2007	4172	2007
A framework for collaborative, content-based and demographic filtering MJ Pazzani Artificial intelligence review 13, 393-408, 1999	2503	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	2258	2001
Learning and revising user profiles: The identification of interesting web sites M Pazzani, D Billsus Machine learning 27, 313-331, 1997	2070	1997
Learning collaborative information filters. D Billsus, MJ Pazzani Icml 98, 46-54, 1998	1932	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	937	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	580	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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