| Canonical correlation analysis: An overview with application to learning methods DR Hardoon, S Szedmak, J Shawe-Taylor Neural computation 16 (12), 2639-2664, 2004 | 3834 | 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 | 436 | 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 | 371 | 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 | 368 | 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 | 301 | 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 | 267 | 2000 |
| Improving" bag-of-keypoints" image categorisation: Generative models and pdf-kernels J Farquhar, S Szedmak, H Meng, J Shawe-Taylor | 164 | 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 |
| 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 | 122 | 2004 |
| 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 | 122 | 1995 |
| 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 | 82 | 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 | 74 | 2018 |
| Kernel-mapping recommender system algorithms MA Ghazanfar, A Prügel-Bennett, S Szedmak Information Sciences 208, 81-104, 2012 | 72 | 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 | 71 | 2003 |
| Liquid-chromatography retention order prediction for metabolite identification E Bach, S Szedmak, C Brouard, S Böcker, J Rousu Bioinformatics 34 (17), i875-i883, 2018 | 69 | 2018 |
| 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 | 46 | 2005 |
| 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 |
| 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 | 38 | 1997 |
John Shawe-TaylorUCL在 cs.ucl.ac.uk 的电子邮件经过验证
