Craig Saunders

Craig's primary research interests lie in Machine Learning, and the development and application of algorithms to a wide variety of application domains. His research has included the development of kernel method algorithms, structured kernels for string and graphs, subspace methods, structured prediction and rigorous confidence estimation techniques.

He has had research and management roles in many EU, UK national and industrial projects, including the EU projects KerMIT, LAVA, SMART, Pinview and PASCAL. Many of these have focussed on basic research into machine learning techniques or the application of such techniques to commercial technologies over many diverse application domains, including geology, bioinformatics, chemoinformatics, music analysis, text categorisation, eye-tracking, image processing and machine translation.

Before joining Xerox in April 2009, Craig held an academic faculty position at the University of Southampton and previous to that a similar position at Royal Holloway, University of London. He gained his PhD in Machine Learning at Royal Holloway in 2000.

Publications

Publications in the XRCE database

Selected publications that are not in the database above:

• Y. Ni, C. Saunders, S. Szedmak, and M. Niranjan. Exploitation of machine learning techniques in modeling phrase movements for machine translation. Journal of Machine Learning Research, 2010.
• Y. Ni, C. Saunders, S. Szedmak, and M. Niranjan. The application of structured learning in natural language processing, Machine Translation Journal 24(2), pages 71-85, Springer, Netherlands, 2010.
• Chu C, Ni Y, Tan G, Saunders CJ, Ashburner J. Kernel regression for fMRI pattern prediction, NeuroImage, Elsevier, 2010
• Bassam Farran, Craig Saunders, and Mahesan Niranjan. Machine learning for intrusion detection: Modeling the distribution shift.In Proceedings of the 2010 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP 2010), 2010
• Ni, Y., Niranjan, M., Saunders, C. and Szedmak, S. Distance phrase reordering for MOSES - User Manual and Code Guide. Technical Report , ISIS Group, School of Electronics and Computer Science, University of Southampton.
• Martin Helmhout, Alisdair McDiarmid, Allan Tomlinson, James Irvine, Craig Saunders, John McDonald, Nigel Jefferies. Instant Knowledge: a Secure Mobile Context-Aware Distributed Recommender System. 2009. In ICT-MobileSummit 2009 Conference Proceedings, Paul Cunningham and Miriam Cunningham (Eds), IIMC International Information Management Corporation.
• Bassam Farran and Craig Saunders, Voted Spheres: An Online, Fast Approach to Large Scale Learning, In proceedings of 23rd International Conference on Advanced Information Networking and Applications, AINA 2009, Bradford, United Kingdom, May 26-29, 2009.
• Pasupa, K., Saunders, C., Szedmak, S., Klami, A., Kaski, S. and Gunn, S. Learning to Rank Images from Eye Movements. In: Proceeding of 2009 IEEE 12th International Conference on Computer Vision (ICCV'2009) Workshop on Human-Computer Interaction (HCI'2009), 27 September - 4 October 2009, Kyoto, Japan. pp. 2009-2016.
• Pasupa, K., Klami, A., Saunders, C., de Campos, T. and Kaski, S, Can relevance of images be inferred from eye movements? In: 15th European Conference on Eye Movements (ECEM'2009), 23-27 August 2009, Southampton, UK
• S. Szedmak, E. Galbrun, C.J. Saunders and Y. Ni, Large scale maximum margin regression based, structural learning approach to phrase translations. In EAMT-2009 Workshop on Statistical Multilingual Analysis for Retrieval and Translation ,Barcelona, Spain, 2009
• Sandor Szedmak, Craig Saunders, Yizhao Ni and Juho Rousu , Max-margin structured output learning in L1 norm space, PASCAL Research Report, 2009
• Yizhao Ni, Carlton Chu, Craig J. Saunders, John Ashburner, Kernel methods for fMRI pattern prediction, In: Proceedings of the International Joint Conference on Neural Networks, IJCNN 2008, part of the IEEE World Congress on Computational Intelligence, WCCI 2008, Hong Kong, China, June 1-6, 2008
• C. Saunders, D. Hardoon, J. Shawe-Taylor, and G. Widmer. Using string kernels to identify famous performers from their playing style. Intelligent Data Analysis, 12(4), 2008.
• J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor. Predicting Structured Data, Chapter, Efficient algorithms for max-margin structured classification. MIT Press, September 2007.
• A. Demco and C. Saunders. Molecular graph kernels for drug discovery. Presented at 6th IARP -TC-15 Workshop on Graph-based Representations in Pattern Recognition, July 2007.
• A. Demco and C. Saunders. Graph kernels for molecular and reduced graphs. UK-QSAR and ChemoInformatics Group, Spring meeting, 2007.
• A. Demco and C. Saunders. Perspectives of Neural-Symbolic Integration, Chapter, Kernels for Strings and Graphs. Springer, October 2007.
• N. Cristianini, J. Shawe-Taylor, and C. Saunders. Kernel methods in Bio-engineering, Image and Signal Processing, Chapter, Kernel Methods: A paradigm for Pattern Analysis. Idea Group, 2006.
• C. Saunders, M. Grobelnik, S. Gunn, and J. Shawe-Taylor, editors. Subspace, Latent Structure and Feature Selection techniques. Springer, 2006.
• D. Hardoon, C. Saunders, S. Szedmak, and J. Shawe-Taylor. A correlation approach for automatic image annotation. In, 2'nd International Conference on Advanced Data Mining and Applications, 2006.
• J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor. Learning hierarchical multicategory text classification models. Journal of Machine Learning Research, 7:1601-1626, 2006.
• B. Hammer, C. Saunders, A. Sperduti, Editors, Special issue on neural networks and kernel methods for structured domains. Journal of Neural Networks, 18(8), 2005.
• J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor. Learning Hierarchical Multi-Category Text Classification Models. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), 2005.
• Barbara Hammer, Craig Saunders, and Alessandro Sperduti. Introduction to the special issue on neural networks and kernel methods for structured domains. Neural Networks, 18(8):1015-1018, 2005.
• S. Szedmak, C. Saunders, J. Shawe-Taylor, and J. Rousu. Learning hierarchy via embedding at two-class complexity. Presented at the NIPS 2005 workshop on Kernel methods for structured domains, 2005.
• A. Vinokourov, A. N. Soklakov, and C. J. Saunders. A probabilistic framework for mismatch and profile string kernels. In Michel Verleysen, editor, Proceedings of the 13th European Symposium on Artificial Neural Networks, pages 325-330, Bruges, Belgium, 2005.
• J. Rousu, C. Saunders, S. Szedmak, and J. Shawe-Taylor. On Maximum Margin Hierarchical Classification. Presented at the NIPS 2004 workshop on Learning with Structured Outputs, 2004.
• Sandor Szedmak, John Shawe-Taylor, Craig Saunders, and David Hardoon. Multiclass classification by l1 norm support vector machine. Pattern Recognition and Machine Learning in Computer Vision Workshop, May 2004.
• C. Saunders, D. Hardoon, J. Shawe-Taylor, and G. Widmer. Using string kernels to identify famous performers from their playing style. In J-F. Boulicaut, F. Esposito, F. Giannotti, and D. Pedreschi, editors, Proceedings of the 15th European Conference on Machine Learning (ECML), LNCS, 384-399. Springer-Verlag Heidelberg, September 2004. Winner of the Best Paper Award, ECML 2004.
• C. Saunders, J. Shawe-Taylor, and A. Vinokourov. String Kernels, Fisher Kernels and Finite State Automata. In S. Becker, S. Thrun, and A. Obermayer, editors, Advances in Neural Information Processing Systems 15, 2003.
• H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C.Watkins. Text Classification using String Kernels. Journal of Machine Learning Research, 2:419-444, 2002.
• C. Saunders, H. Tschach, and J. Shawe-Taylor. Syllables and Other String Kernel extensions. In Proceedings of the Nineteenth International Conference on Machine Learning (ICML '02), 2002.
• T. Melluish, C. Saunders, I. Nouretdinov, and V. Vovk. Comparing the Bayes and Typicalness Frameworks. In Proceedings of the 12th European Conference on Machine Learning (ECML-2001), Lecture Notes in Computer Science. Springer-Verlag, 2001.
• T. Melluish, C. Saunders, I. Nouretdinov, and V. Vovk. Comparing the Bayes and Typicalness Frameworks. Technical Report CLRC-01-05, Computer Learning Research Centre, Royal Holloway University of London, 2001.
• C. Saunders, A. Gammerman, H. Brown, and G. Donald. Application of Support Vector Machines to Fault Diagnosis and Automated Repair. In Proceedings of the Eleventh International Workshop on Principles of Diagnosis (DX '00), 2000.
• C. Saunders, A. Gammerman, and V. Vovk. Computationally Efficient Transductive Machines. In Proceedings of the Eleventh International Conference on algorithmic Learning Theory 2000 (ALT '00), Lecture Notes in Artificial Intelligenece. Springer-Verlag, 2000.
• C. Saunders. Efficient Implementation and Experimental Testing of Transductive Algorithms for Predicting with Confidence. PhD thesis, Royal Holloway, University of London, 2000.
• Nicolas Gilardi, Alex Gammerman, Mikhail Kanevski, Michel Maignan, Tom Melluish, Craig Saunders and Volodia Vovk. Application des Méthodes d'Apprentissage pour l'Etude des Risques de Pollution dans le Lac Léman, Colloque CLUSE sur les Risques Majeurs, 2000.
• V. Vovk, A. Gammerman, and C. Saunders. Machine-Learning Applications of Algorithmic Randomness. In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-1999), pages 444-453, 1999.
• C. Saunders, A. Gammerman, and V. Vovk. Transduction with Confidence and Credibility. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI '99), volume 2, pages 722-726, 1999.
• C. Saunders, A. Gammerman, and V. Vovk. Ridge Regression Learning Algorithm in Dual Variables. In (ICML-1998) Proceedings of the 15th International Conference on Machine Learning, pages 515-521. Morgan Kaufmann, 1998.
• C. Saunders, M.O. Stitson, J. Weston, L. Bottou, B. Schölkopf, and A. Smola. Support Vector Machine - Reference Manual. Technical Report CSD-TR-98-03, Royal Holloway, University of London, 1998.