Sandor Szedmak

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John Shawe-TaylorUCLVerified email at cs.ucl.ac.uk
Juho RousuProfessor, Department of Computer Science, Aalto UniversityVerified email at aalto.fi
Justus PiaterProfessor of Computer Science, University of Innsbruck, AustriaVerified email at uibk.ac.at
Craig SaundersAmazon IncVerified email at amazon.com
Hongying Meng (孟鸿鹰)Professor, Brunel University of LondonVerified email at brunel.ac.uk
Jason FarquharDonders Institute, Radboud University NijmegenVerified email at donders.ru.nl
Emre UgurBogazici UniversityVerified email at boun.edu.tr
Yizhao NICoBankVerified email at cobank.com
Antonio Rodriguez-SanchezAssociate Professor, University of InnsbruckVerified email at uibk.ac.at
Kitsuchart PasupaSchool of Information Technology, King Mongkut's Institute of Technology LadkrabangVerified email at it.kmitl.ac.th
Esa PitkänenInstitute for Molecular Medicine Finland (FIMM), University of HelsinkiVerified email at helsinki.fi
Steve GunnProfessor of Electronics and Computer Science, University of SouthamptonVerified email at ecs.soton.ac.uk
Simon HanglUniversity of InnsbruckVerified email at student.uibk.ac.at
Adam Prugel-BennettUniversity of SouthamptonVerified email at ecs.soton.ac.uk
Mustansar ali GhazanfarUniversity of East LondonVerified email at uel.ac.uk
Senka KrivićAssistant Professor at Faculty of Electrical Engineering, University of SarajevoVerified email at etf.unsa.ba
Céline BrouardINRAE, MIA Toulouse, FranceVerified email at inra.fr
Mahesan NiranjanUniversity of SouthamptonVerified email at ecs.soton.ac.uk
Wail MustafaTechnical University of DenmarkVerified email at dtu.dk
Alexander KoganRutgers Business SchoolVerified email at rutgers.edu

Sandor Szedmak

Department of Computer Science, Aalto University

Verified email at aalto.fi

machine learning applied mathematics


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Canonical correlation analysis: An overview with application to learning methods DR Hardoon, S Szedmak, J Shawe-Taylor Neural computation 16 (12), 2639-2664, 2004	3928	2004
Two view learning: SVM-2K, theory and practice J Farquhar, D Hardoon, H Meng, J Shawe-Taylor, S Szedmak Advances in neural information processing systems 18, 2005	447	2005
Kernel-based learning of hierarchical multilabel classification models J Rousu, C Saunders, S Szedmak, J Shawe-Taylor Journal of Machine Learning Research 7, 1601-1626, 2006	374	2006
The 2005 pascal visual object classes challenge M Everingham, A Zisserman, CKI Williams, L Van Gool, M Allan, ... Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual …, 2006	373	2006
Depressive symptomatology and vital exhaustion are differentially related to behavioral risk factors for coronary artery disease MS Kopp, PRJ Falger, AD Appels, S Szedmak Psychosomatic medicine 60 (6), 752-758, 1998	304	1998
Psychosocial risk factors, inequality and self-rated morbidity in a changing society MS Kopp, Á Skrabski, S Szedmák Social science & medicine 51 (9), 1351-1361, 2000	274	2000
Improving" bag-of-keypoints" image categorisation: Generative models and pdf-kernels J Farquhar, S Szedmak, H Meng, J Shawe-Taylor	165	2005
Learning hierarchical multi-category text classification models J Rousu, C Saunders, S Szedmak, J Shawe-Taylor Proceedings of the 22nd international conference on Machine learning, 744-751, 2005	124	2005
Socioeconomic factors, severity of depressive symptomatology, and sickness absence rate in the Hungarian population MS Kopp, Á Skrabski, S Szedmák Journal of Psychosomatic Research 39 (8), 1019-1029, 1995	124	1995
Pareto-Optimal Patterns in Logical Analysis of Data SS P.L. Hammer, A. Kogan, B. Simeone Discrete Applied Mathematics 144 (1-2), 79-102, 2004	123	2004
Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects H Julkunen, A Cichonska, P Gautam, S Szedmak, J Douat, T Pahikkala, ... Nature communications 11 (1), 6136, 2020	92	2020
Learning with multiple pairwise kernels for drug bioactivity prediction A Cichonska, T Pahikkala, S Szedmak, H Julkunen, A Airola, M Heinonen, ... Bioinformatics 34 (13), i509-i518, 2018	78	2018
Liquid-chromatography retention order prediction for metabolite identification E Bach, S Szedmak, C Brouard, S Böcker, J Rousu Bioinformatics 34 (17), i875-i883, 2018	73	2018
Kernel-mapping recommender system algorithms MA Ghazanfar, A Prügel-Bennett, S Szedmak Information Sciences 208, 81-104, 2012	73	2012
Severity of allergic complaints: the importance of depressed mood M Kovács, A Stauder, S Szedmák Journal of psychosomatic research 54 (6), 549-557, 2003	73	2003
A correlation approach for automatic image annotation DR Hardoon, C Saunders, S Szedmak, J Shawe-Taylor International Conference on Advanced Data Mining and Applications, 681-692, 2006	69	2006
Towards structured output prediction of enzyme function K Astikainen, L Holm, E Pitkänen, S Szedmak, J Rousu BMC proceedings 2, 1-10, 2008	60	2008
Learning via linear operators: Maximum margin regression S Szedmak, J Shawe-Taylor, E Parado-Hernandez In Proceedings of 2001 IEEE International Conference on Data Mining. Citeseer, 2005	47	2005
A depressziós tünetegyüttes gyakorisága és egészségügyi jelentősége a magyar lakosság körében M Kopp, S Szedmák, J Lőke, Á Skrabski Lege Artis Med 3, 136-44, 1997	41	1997
Socioeconomic differences and psychosocial aspects of stress in a changing society MS Kopp, S Szedmák, A Skrabski ANNALS-NEW YORK ACADEMY OF SCIENCES 851, 538-543, 1998	39	1998

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