Sándor Szedmák

Mustansar Ali Ghazanfar, Adam Prügel-Bennett, Sándor Szedmák: Kernel-Mapping Recommender system algorithms. Inf. Sci. 208: 81-104 (2012)

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan: Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation. Journal of Machine Learning Research 12: 1-30 (2011)

Mustansar Ali Ghazanfar, Sándor Szedmák, Adam Prügel-Bennett: Incremental Kernel Mapping Algorithms for Scalable Recommender Systems. ICTAI 2011: 1077-1084

Sándor Szedmák, Yizhao Ni, Steve R. Gunn: Maximum Margin Learning with Incomplete Data: Learning Networks instead of Tables. Journal of Machine Learning Research - Proceedings Track 11: 96-102 (2010)

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan: The application of structured learning in natural language processing. Machine Translation 24(2): 71-85 (2010)

Katja Astikainen, Esa Pitkänen, Juho Rousu, Liisa Holm, Sándor Szedmák: Reaction Kernels - Structured Output Prediction Approaches for Novel Enzyme Function. BIOINFORMATICS 2010: 48-55

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan: Handling phrase reorderings for machine translation. ACL/IJCNLP (Short Papers) 2009: 241-244

Sándor Szedmák, John Shawe-Taylor: Synthesis of maximum margin and multiview learning using unlabeled data. Neurocomputing 70(7-9): 1254-1264 (2007)

Sándor Szedmák, Tijl De Bie, David R. Hardoon: A metamorphosis of Canonical Correlation Analysis into multivariate maximum margin learning. ESANN 2007: 211-216

Zhuoran Wang, John Shawe-Taylor, Sándor Szedmák: Kernel Regression Based Machine Translation. HLT-NAACL (Short Papers) 2007: 185-188

Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor: Kernel-Based Learning of Hierarchical Multilabel Classification Models. Journal of Machine Learning Research 7: 1601-1626 (2006)

David R. Hardoon, Craig Saunders, Sándor Szedmák, John Shawe-Taylor: A Correlation Approach for Automatic Image Annotation. ADMA 2006: 681-692

Sándor Szedmák, John Shawe-Taylor: Synthesis of maximum margin and multiview learning using unlabeled data. ESANN 2006: 479-484

Juho Rousu, Craig Saunders, Sándor Szedmák, John Shawe-Taylor: Learning hierarchical multi-category text classification models. ICML 2005: 744-751

Mark Everingham, Andrew Zisserman, Christopher K. I. Williams, Luc J. Van Gool, Moray Allan, Christopher M. Bishop, Olivier Chapelle, Navneet Dalal, Thomas Deselaers, Gyuri Dorkó, Stefan Duffner, Jan Eichhorn, Jason D. R. Farquhar, Mario Fritz, Christophe Garcia, Thomas L. Griffiths, Frédéric Jurie, Daniel Keysers, Markus Koskela, Jorma Laaksonen, Diane Larlus, Bastian Leibe, Hongying Meng, Hermann Ney, Bernt Schiele, Cordelia Schmid, Edgar Seemann, John Shawe-Taylor, Amos J. Storkey, Sándor Szedmák, Bill Triggs, Ilkay Ulusoy, Ville Viitaniemi, Jianguo Zhang: The 2005 PASCAL Visual Object Classes Challenge. MLCW 2005: 117-176

Jason D. R. Farquhar, David R. Hardoon, Hongying Meng, John Shawe-Taylor, Sándor Szedmák: Two view learning: SVM-2K, Theory and Practice. NIPS 2005

Peter L. Hammer, Alexander Kogan, Bruno Simeone, Sándor Szedmák: Pareto-optimal patterns in logical analysis of data. Discrete Applied Mathematics 144(1-2): 79-102 (2004)

Peter L. Hammer, Yanpei Liu, Bruno Simeone, Sándor Szedmák: Saturated systems of homogeneous boxes and the logical analysis of numerical data. Discrete Applied Mathematics 144(1-2): 103-109 (2004)

David R. Hardoon, Sándor Szedmák, John Shawe-Taylor: Canonical Correlation Analysis: An Overview with Application to Learning Methods. Neural Computation 16(12): 2639-2664 (2004)

Hongying Meng, John Shawe-Taylor, Sándor Szedmák, Jason D. R. Farquhar: Support Vector Machine to Synthesise Kernels. Deterministic and Statistical Methods in Machine Learning 2004: 242-255