Michael J. Pazzani

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	Semua	Sejak 2020
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Eamonn KeoghDistinguished Professor of Computer Science, University of California - RiversideEmail yang diverifikasi di cs.ucr.edu
Selina ChuResearch Scientist, Nasa Jet Propulsion Laboratory, California Institute of TechnologyEmail yang diverifikasi di jpl.nasa.gov
Pedro DomingosProfessor of Computer Science and Engineering, University of WashingtonEmail yang diverifikasi di cs.washington.edu
Chris MerzMastercardEmail yang diverifikasi di mastercard.com
Subramani ManiFormer faculty -- UNM-Albuquerque, Vanderbilt, UW-MilwaukeeEmail yang diverifikasi di salud.unm.edu
William ShankleUC Irvine Cognitive ScienceEmail yang diverifikasi di shankleclinic.com
Sharad MehrotraDistinguished Professor of Computer Science, Univ. of California, IrvineEmail yang diverifikasi di ics.uci.edu
Kamal AliSimplifaiEmail yang diverifikasi di simplifai.ai
Padhraic SmythDistinguished Professor, Computer Science, University of California IrvineEmail yang diverifikasi di ics.uci.edu
Douglas FisherVanderbilt UniversityEmail yang diverifikasi di vanderbilt.edu
Geoff WebbProfessor, Department of Data Science and Artificial Intelligence, Monash UniversityEmail yang diverifikasi di monash.edu
Pat LangleyDirector, ISLE; Research Scientist, Stanford UniversityEmail yang diverifikasi di stanford.edu
Mark AckermanUniversity of Michigan, Ann ArborEmail yang diverifikasi di umich.edu
Kamran AlipourApple Inc.Email yang diverifikasi di apple.com
Chelsea DobbinsThe University of QueenslandEmail yang diverifikasi di uq.edu.au
Matt JonesProfessor of Computer Science, Computational Foundry, Swansea UniversityEmail yang diverifikasi di swansea.ac.uk
George BuchananDeputy Dean of Research, School of Computing Technologies, RMIT UniversityEmail yang diverifikasi di rmit.edu.au
Harold ThimblebyProfessor of Computer Science, Swansea UniversityEmail yang diverifikasi di swansea.ac.uk
Gary MarsdenProfessor of Computer Science, University of Cape TownEmail yang diverifikasi di cs.uct.ac.za
Keli XiaoStony Brook UniversityEmail yang diverifikasi di stonybrook.edu

Ikuti

Michael J. Pazzani

Distinguished Principal Scientist

Email yang diverifikasi di isi.edu - Beranda

Machine Learning Explainable Artificial Intelligence Personalization Recommender Systems Search


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
On the optimality of the simple Bayesian classifier under zero-one loss P Domingos, M Pazzani Machine learning 29 (2-3), 103-130, 1997	4743	1997
Content-Based Recommendation Systems MJ Pazzani Springer Link, 2007	4158	2007
A framework for collaborative, content-based and demographic filtering MJ Pazzani Artificial intelligence review 13, 393-408, 1999	2495	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	2253	2001
Learning and revising user profiles: The identification of interesting web sites M Pazzani, D Billsus Machine learning 27, 313-331, 1997	2082	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	1724	2001
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	1261	2001
Syskill & Webert: Identifying interesting web sites MJ Pazzani, J Muramatsu, D Billsus AAAI/IAAI, Vol. 1, 54-61, 1996	1261	1996
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	1240	1996
Scaling up dynamic time warping for datamining applications EJ Keogh, MJ Pazzani Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	1148	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	961	2004
User modeling for adaptive news access D Billsus, MJ Pazzani User modeling and user-adapted interaction 10, 147-180, 2000	942	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	851	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	692	1999
Machine learning for user modeling GI Webb, MJ Pazzani, D Billsus User modeling and user-adapted interaction 11, 19-29, 2001	652	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	582	2002
Detecting group differences: Mining contrast sets SD Bay, MJ Pazzani Data mining and knowledge discovery 5, 213-246, 2001	558	2001
Reducing misclassification costs M Pazzani, C Merz, P Murphy, K Ali, T Hume, C Brunk Machine Learning Proceedings 1994, 217-225, 1994	541	1994
The utility of knowledge in inductive learning M Pazzani, D Kibler Machine learning 9, 57-94, 1992	508	1992

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