Kiri Wagstaff

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	总计	2020 年至今
引用	10667	3348
h 指数	38	24
i10 指数	80	48

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David R. ThompsonJet Propulsion Laboratory, California Institute of Technology在 jpl.nasa.gov 的电子邮件经过验证
Steve ChienJPL Fellow, Jet Propulsion Laboratory, Calif. Inst. Tech.在 jpl.nasa.gov 的电子邮件经过验证
Claire CardieProfessor of Computer Science, Cornell University在 cs.cornell.edu 的电子邮件经过验证
Gary DoranJet Propulsion Laboratory在 jpl.nasa.gov 的电子邮件经过验证
B. BueResearch Data Scientist, Jet Propulsion Laboratory在 jpl.nasa.gov 的电子邮件经过验证
Umaa RebbapragadaJet Propulsion Laboratory在 jpl.nasa.gov 的电子邮件经过验证
Stefan SchroedlAtomwise, Inc在 atomwise.com 的电子邮件经过验证
Lukas MandrakeJet Propulsion Laboratory在 jpl.nasa.gov 的电子邮件经过验证
Randall WaythSKA-Low Senior Commissioning Scientist在 skao.int 的电子邮件经过验证
Adam DellerCentre for Astrophysics & Supercomputing, Swinburne University of Technology在 astro.swin.edu.au 的电子邮件经过验证
Steven TingayProfessor of Radio Astronomy, Curtin University在 curtin.edu.au 的电子邮件经过验证
Marie desJardinsDean and Professor of Computer Science在 simmons.edu 的电子邮件经过验证
Hannah Rae KernerAssistant Professor, Arizona State University在 umd.edu 的电子邮件经过验证
Ian DavidsonProfessor of Computer Science, University of California - Davis在 cs.ucdavis.edu 的电子邮件经过验证
Sugato BasuDistinguished Scientist at Google在 google.com 的电子邮件经过验证
Benyang TangJet Propulsion Laboratory在 jpl.nasa.gov 的电子邮件经过验证
Thomas J. FuchsIcahn School of Medicine at Mount Sinai在 mssm.edu 的电子邮件经过验证
Heni Ben AmorAssociate Professor, Arizona State University在 asu.edu 的电子邮件经过验证
Dmitriy BekkerJohns Hopkins University Applied Physics Laboratory在 jhuapl.edu 的电子邮件经过验证
Heather Terran Laneathenahealth, Inc.在 illation.net 的电子邮件经过验证

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Machine Learning Geology Astronomy Education Library Science


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
Constrained k-means clustering with background knowledge K Wagstaff, C Cardie, S Rogers, S Schrödl Icml 1, 577-584, 2001	4052	2001
Clustering with instance-level constraints K Wagstaff, C Cardie AAAI/IAAI 1097 (577-584), 197, 2000	908	2000
Constrained clustering: Advances in algorithms, theory, and applications S Basu, I Davidson, K Wagstaff Chapman and Hall/CRC, 2008	764	2008
Machine learning that matters K Wagstaff Proceedings of the Twenty-Ninth International Conference on Machine Learning …, 2012	434	2012
Mining GPS traces for map refinement S Schroedl, K Wagstaff, S Rogers, P Langley, C Wilson Data mining and knowledge Discovery 9, 59-87, 2004	333	2004
Noun phrase coreference as clustering C Cardie, K Wagstaff 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language …, 1999	326	1999
Measuring constraint-set utility for partitional clustering algorithms I Davidson, KL Wagstaff, S Basu European conference on principles of data mining and knowledge discovery …, 2006	288	2006
Machine learning for science and society C Rudin, KL Wagstaff Machine Learning 95, 1-9, 2014	168	2014
The commensal real-time ASKAP fast-transients (CRAFT) survey JP Macquart, M Bailes, NDR Bhat, GC Bower, JD Bunton, S Chatterjee, ... Publications of the Astronomical Society of Australia 27 (3), 272-282, 2010	165	2010
VAST: an ASKAP survey for variables and slow transients T Murphy, S Chatterjee, DL Kaplan, J Banyer, ME Bell, HE Bignall, ... Publications of the Astronomical Society of Australia 30, e006, 2013	152	2013
Multidocument summarization via information extraction M White, T Korelsky, C Cardie, V Ng, D Pierce, K Wagstaff Proceedings of the first international conference on Human language …, 2001	149	2001
Intelligent clustering with instance-level constraints KL Wagstaff Cornell University, 2002	139	2002
Clustering with missing values: no imputation required. K Wagstaff Pasadena, CA: Jet Propulsion Laboratory, National Aeronautics and Space …, 2004	116	2004
Alpha seeding for support vector machines D DeCoste, K Wagstaff Proceedings of the sixth ACM SIGKDD international conference on Knowledge …, 2000	110	2000
Onboard autonomy on the intelligent payload experiment cubesat mission S Chien, J Doubleday, DR Thompson, KL Wagstaff, J Bellardo, C Francis, ... Journal of Aerospace Information Systems 14 (6), 307-315, 2017	107	2017
When is constrained clustering beneficial, and why KL Wagstaff, S Basu, I Davidson Ionosphere 58 (60.1), 62-63, 2006	92	2006
Active constrained clustering by examining spectral eigenvectors Q Xu, M desJardins, KL Wagstaff Discovery Science: 8th International Conference, DS 2005, Singapore, October …, 2005	84	2005
V-fastr: The vlba fast radio transients experiment RB Wayth, WF Brisken, AT Deller, WA Majid, DR Thompson, SJ Tingay, ... The Astrophysical Journal 735 (2), 97, 2011	75	2011
Deep mars: Cnn classification of mars imagery for the pds imaging atlas K Wagstaff, Y Lu, A Stanboli, K Grimes, T Gowda, J Padams Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018	73	2018
Constrained Spectral Clustering under a Local Proximity Structure Assumption. Q Xu, Marie desJardins, K Wagstaff FLAIRS, 866-867, 2005	63	2005

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