Koby Crammer 个人学术档案 - 学术资源搜索

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	总计	2019 年至今
引用	23176	9731
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Fernando PereiraVP, Google在 google.com 的电子邮件经过验证
Mark DredzeJohns Hopkins University在 cs.jhu.edu 的电子邮件经过验证
Alex KuleszaResearch Scientist, Google在 google.com 的电子邮件经过验证
Jennifer Wortman VaughanSenior Principal Researcher, Microsoft Research, New York City在 microsoft.com 的电子邮件经过验证
Partha TalukdarGoogle Research and IISc Bangalore在 iisc.ac.in 的电子邮件经过验证
Shai Ben-DavidUniverity of Waterloo, Canada在 uwaterloo.ca 的电子邮件经过验证
Joseph KeshetProfessor, Faculty of Electrical & Computer Engineering, Technion在 technion.ac.il 的电子邮件经过验证
Shai Shalev-ShwartzThe Hebrew University在 cs.huji.ac.il 的电子邮件经过验证
Ofer DekelMicrosoft Research在 microsoft.com 的电子邮件经过验证
Shie MannorProfessor of Electrical Engineering @ Technion & Researcher @ Nvidia在 technion.ac.il 的电子邮件经过验证
Michael KearnsProfessor of Computer Science, University of Pennsylvania在 cis.upenn.edu 的电子邮件经过验证
Slobodan VuceticProfessor of Computer Science, Temple University在 temple.edu 的电子邮件经过验证
Hadas BenistyTechnion在 technion.ac.il 的电子邮件经过验证
Ran El-YanivProfessor of Computer Science, Technion - Israel Institute of Technology. Chief Scientist - Deci AI在 cs.technion.ac.il 的电子邮件经过验证
David MalahProfessor Emeritus, Electrical and Computer Engineering, Technion - I.I.T在 ee.technion.ac.il 的电子邮件经过验证
Peter BartlettProfessor, EECS and Statistics, UC Berkeley在 cs.berkeley.edu 的电子邮件经过验证
Yevgeny SeldinProfessor, Department of Computer Science, University of Copenhagen在 di.ku.dk 的电子邮件经过验证
Kuzman GanchevGoogle Inc在 google.com 的电子邮件经过验证
Francesco OrabonaAssociate Professor, KAUST在 orabona.com 的电子邮件经过验证
Axel BernalData Scientist, Human Longevity Inc在 humanlongevity.com 的电子邮件经过验证

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Koby Crammer

Professor of Electrical Engineering, Technion - Israel Institute of Technology

在 ee.technion.ac.il 的电子邮件经过验证

Machine Learning Big Data


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
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	3805	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	3005	2001
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	2595	2006
Analysis of representations for domain adaptation S Ben-David, J Blitzer, K Crammer, F Pereira Advances in neural information processing systems, 137-144, 2007	2591	2007
Online large-margin training of dependency parsers R McDonald, K Crammer, F Pereira Proceedings of the 43rd annual meeting on association for computational …, 2005	1030	2005
On the learnability and design of output codes for multiclass problems K Crammer, Y Singer Machine learning 47 (2-3), 201-233, 2002	996	2002
Pranking with ranking K Crammer, Y Singer Advances in neural information processing systems, 641-647, 2002	899	2002
Ultraconservative online algorithms for multiclass problems K Crammer, Y Singer Journal of Machine Learning Research 3 (Jan), 951-991, 2003	769	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	587	2008
Confidence-weighted linear classification M Dredze, K Crammer, F Pereira Proceedings of the 25th international conference on Machine learning, 264-271, 2008	578	2008
Adaptive regularization of weight vectors K Crammer, A Kulesza, M Dredze Advances in neural information processing systems, 414-422, 2009	396	2009
Learning from multiple sources K Crammer, M Kearns, J Wortman Journal of Machine Learning Research 9 (Aug), 1757-1774, 2008	332	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	323	2003
New regularized algorithms for transductive learning PP Talukdar, K Crammer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2009	299	2009
A family of additive online algorithms for category ranking K Crammer, Y Singer Journal of Machine Learning Research 3 (Feb), 1025-1058, 2003	261	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	250	2012
Robust support vector machine training via convex outlier ablation L Xu, K Crammer, D Schuurmans AAAI 6, 536-542, 2006	214	2006
Online classification on a budget K Crammer, J Kandola, Y Singer Advances in neural information processing systems, 225-232, 2004	211	2004
Exact convex confidence-weighted learning K Crammer, M Dredze, F Pereira Advances in Neural Information Processing Systems 21, 2008	189	2008
Kernel design using boosting K Crammer, J Keshet, Y Singer Advances in neural information processing systems, 553-560, 2003	184	2003

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