Koby Crammer

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Fernando PereiraVP, GoogleEmail verificata su google.com
Mark DredzeJohns Hopkins UniversityEmail verificata su cs.jhu.edu
Alex KuleszaResearch Scientist, GoogleEmail verificata su google.com
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York CityEmail verificata su microsoft.com
Partha TalukdarGoogle Research and IISc BangaloreEmail verificata su iisc.ac.in
Shai Ben-DavidUniverity of Waterloo, CanadaEmail verificata su uwaterloo.ca
Joseph KeshetProfessor, Faculty of Electrical & Computer Engineering, TechnionEmail verificata su technion.ac.il
Shai Shalev-ShwartzThe Hebrew UniversityEmail verificata su cs.huji.ac.il
Ofer DekelMicrosoft ResearchEmail verificata su microsoft.com
Shie MannorProfessor of Electrical Engineering @ Technion & Researcher @ NvidiaEmail verificata su technion.ac.il
Michael KearnsProfessor of Computer Science, University of PennsylvaniaEmail verificata su cis.upenn.edu
Slobodan VuceticProfessor of Computer Science, Temple UniversityEmail verificata su temple.edu
Hadas BenistyTechnionEmail verificata su technion.ac.il
Ran El-YanivProfessor of Computer Science, Technion - Israel Institute of Technology. Chief Scientist - Deci AIEmail verificata su cs.technion.ac.il
David MalahProfessor Emeritus, Electrical and Computer Engineering, Technion - I.I.TEmail verificata su ee.technion.ac.il
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyEmail verificata su cs.berkeley.edu
Yevgeny SeldinProfessor, Department of Computer Science, University of CopenhagenEmail verificata su di.ku.dk
Kuzman GanchevGoogle IncEmail verificata su google.com
Francesco OrabonaAssociate Professor, KAUSTEmail verificata su orabona.com
Axel BernalData Scientist, Human Longevity IncEmail verificata su humanlongevity.com

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Koby Crammer

Professor of Electrical Engineering, Technion - Israel Institute of Technology

Email verificata su ee.technion.ac.il

Machine Learning Big Data


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
A theory of learning from different domains S Ben-David, J Blitzer, K Crammer, A Kulesza, F Pereira, JW Vaughan Machine learning 79 (1-2), 151-175, 2010	4028	2010
On the algorithmic implementation of multiclass kernel-based vector machines K Crammer, Y Singer Journal of machine learning research 2 (Dec), 265-292, 2001	3041	2001
Analysis of representations for domain adaptation S Ben-David, J Blitzer, K Crammer, F Pereira Advances in neural information processing systems, 137-144, 2007	2741	2007
Online passive-aggressive algorithms K Crammer, O Dekel, J Keshet, S Shalev-Shwartz, Y Singer Journal of Machine Learning Research 7 (Mar), 551-585, 2006	2641	2006
Online large-margin training of dependency parsers R McDonald, K Crammer, F Pereira Proceedings of the 43rd annual meeting on association for computational …, 2005	1037	2005
On the learnability and design of output codes for multiclass problems K Crammer, Y Singer Machine learning 47 (2-3), 201-233, 2002	1003	2002
Pranking with ranking K Crammer, Y Singer Advances in neural information processing systems, 641-647, 2002	906	2002
Ultraconservative online algorithms for multiclass problems K Crammer, Y Singer Journal of Machine Learning Research 3 (Jan), 951-991, 2003	774	2003
Learning bounds for domain adaptation J Blitzer, K Crammer, A Kulesza, F Pereira, J Wortman Advances in neural information processing systems, 129-136, 2008	608	2008
Confidence-weighted linear classification M Dredze, K Crammer, F Pereira Proceedings of the 25th international conference on Machine learning, 264-271, 2008	587	2008
Adaptive regularization of weight vectors K Crammer, A Kulesza, M Dredze Advances in neural information processing systems, 414-422, 2009	402	2009
Learning from multiple sources K Crammer, M Kearns, J Wortman Journal of Machine Learning Research 9 (Aug), 1757-1774, 2008	339	2008
Margin analysis of the LVQ algorithm K Crammer, R Gilad-Bachrach, A Navot, N Tishby Advances in neural information processing systems, 479-486, 2003	327	2003
New regularized algorithms for transductive learning PP Talukdar, K Crammer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2009	302	2009
A family of additive online algorithms for category ranking K Crammer, Y Singer Journal of Machine Learning Research 3 (Feb), 1025-1058, 2003	262	2003
Breaking the curse of kernelization: Budgeted stochastic gradient descent for large-scale svm training Z Wang, K Crammer, S Vucetic Journal of Machine Learning Research 13 (Oct), 3103-3131, 2012	256	2012
Robust support vector machine training via convex outlier ablation L Xu, K Crammer, D Schuurmans AAAI 6, 536-542, 2006	218	2006
Online classification on a budget K Crammer, J Kandola, Y Singer Advances in neural information processing systems, 225-232, 2004	212	2004
Exact convex confidence-weighted learning K Crammer, M Dredze, F Pereira Advances in Neural Information Processing Systems 21, 2008	190	2008
Kernel design using boosting K Crammer, J Keshet, Y Singer Advances in neural information processing systems, 553-560, 2003	185	2003

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