Irina M. Armean, Kathryn S. Lilley, Matthew W. B. Trotter, Nicholas C. V. Pilkington and Sean B. Holden (2018). Co-complex protein membership evaluation using Maximum Entropy on GO ontology and InterPro annotation. Bioinformatics, https://doi.org/10.1093/bioinformatics/btx803.
Lisa M. Breckels, Sean Holden, David Wojnar, Claire M. Mulvey, Andy Christoforou, Arnoud Groen, Matthew W.B. Trotter, Oliver Kohlbacher, Kathryn S. Lilley and Laurent Gatto (2016). Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics. PLoS Comput Biol 12(5): e1004920. doi:10.1371/journal.pcbi.1004920
Sean B. Holden. HasGP: A Haskell library for Gaussian process inference. Technical Report UCAM-CL-TR-885, University of Cambridge, Computer Laboratory, April 2016.
Richard Russell and Sean B. Holden. Survey propagation applied to weighted partial maximum satisfiability. Technical Report UCAM-CL-TR-883, University of Cambridge, Computer Laboratory, March 2016.
Kiran K. Rachuri, Theus Hossmann, Cecilia Mascolo and Sean B. Holden. Beyond Location Check-ins: Exploring Physical and Soft Sensing to Augment Social Check-in Apps. Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom) 2015, pages 123-130.
Ivo Timoteo and Sean B Holden. Learning Dynamic Systems From Time-series Data – An Application to Gene Regulatory Networks. Proceedings of the 4th International Conference on Pattern Recognition Applications and Methods (ICPRAM) 2015.
James P. Bridge, Sean B. Holden and Lawrence C. Paulson. Machine learning for first-order theorem proving: learning to select a good heuristic. Journal of Automated Reasoning, 2014. Available online.
Nicholas Pilkington, Sean B. Holden and Matthew W. B. Trotter. Multiple Kernel Learning for Drug Discovery. Molecular Informatics, Volume 31, Issue 3-4, pages 313-322, 2012.
Sean B. Holden. The HasGP user manual. Technical report number UCAM-CL-TR-804, University of Cambridge, Computer Laboratory, September 2011.
Richard Russell and Sean Holden. Handling Goal Utility Dependencies in a Satisfiability Framework. Proceedings of International Conference on Automated Planning and Scheduling (ICAPS), 2010.
Simon Fothergill, Rob Harle and Sean Holden. Modelling the Model Athlete : Automatic Coaching of Rowing Technique. 12th International Workshop on Structural and Syntactic Pattern Recognition, 7th International Workshop on Statistical Pattern Recognition (S+SSPR), 2008.
Andrew Naish-Guzman and Sean Holden. The Generalized FITC Approximation. Proceedings of Neural Information Processing Systems (NIPS), 2007.
Andrew Naish-Guzman and Sean Holden. Robust Regression with Twinned Gaussian Processes. Proceedings of Neural Information Processing Systems (NIPS), 2007.
Ulrich Paquet, Sean Holden and Andrew Naish-Guzman. Bayesian Hierarchical Ordinal Regression. Proceedings of the International Conference on Artificial Neural Networks (ICANN), 2005.
Andrew Naish-Guzman, Sean Holden and Ulrich Paquet. On The Explicit Use Of Example Weights In The Construction Of Classifiers. Proceedings of the International Conference on Artificial Neural Networks (ICANN), 2005.
P. Hammond, T. J. Hutton, J. E. Allanson, L. E. Campbell, R. C. M. Hennekam, S. Holden, K. C. Murphy, M. A. Patton, A. Shaw, I. K. Temple, M. Trotter, R. M. Winter. 3D Analysis of Facial Morphology. American Journal of Medical Genetics, Volume 126A, Number 4, May 2004, pages 339-348.
M. Trotter and S. Holden. Support Vector Machines for ADME Property Classification. QSAR and Combinatorial Science, Volume 22, Number 5, July 2003, pages 533-548.
R. Burbidge, M. Trotter, S. B. Holden and B. Buxton. Drug Design by Machine Learning: Support Vector Machines for Pharmaceutical Data. Special issue of Computers and Chemistry, Volume 26, Number 1, December 2001, pages 4-15.
J. Wickramaratna, S. B. Holden and B. Buxton.Performance Degradation in Boosting. Proceedings of the 2nd International Workshop on Multiple Classifier Systems, Editors Josef Kittler and Fabio Roli, Cambridge, United Kingdom, 2001. Lecture Notes in Computer Science 2096, pages 11-21.
M. Trotter, B. Buxton and S. B. Holden. Support Vector Machines in Combinatorial Chemistry. Measurement and Control, Volume 34, Number 8, pages 235-239, October 2001.
R. Burbidge, M. Trotter, B. Buxton and S. B. Holden. STAR - Sparsity Through Automated Rejection. Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence: 6th International Work-Conference On Artificial and Natural Neural Networks , IWANN 2001, Proceedings, Part 1., Granada, Spain. Lecture Notes in Computer Science 2084, pages 653-660, editors J. Mira and A. Prieto, Springer-Verlag.
M. Anthony and S. B. Holden. Cross-Validation for Binary Classification by Real-Valued Functions: Theoretical Analysis. Proceedings of the Eleventh Annual Conference on Computational Learning Theory (COLT) 1998, pages 218-229.
S. B. Holden. Generalization. Statistics and Computing, Volume 8, Number 1, March 1998, pages 3-4.
S. B. Holden. On Algorithmic Stability and the Analysis of the Cross-Validation and Holdout Estimates. Research Note RN/97/73, 1998. Department of Computer Science, University College London.
S. B. Holden and M. Niranjan. Average-Case Learning Curves for Radial Basis Function Networks. Neural Computation, Volume 9, Number 2, February 1997, pages 441-460.
S. B. Holden. PAC-like Upper Bounds for the Sample Complexity of Leave-One-Out Cross-Validation. Proceedings of the Ninth Annual Conference on Computational Learning Theory (COLT) 1996, pages 41-50.
M. Price, S. B. Holden and M Sandler. Accurate Parallel Form Filter Synthesis. Electronics Letters, Volume 32, Number 22, October 1996, pages 2066-2067.
S. B. Holden. Cross-Validation and the PAC Learning Model. Research Note RN/96/64, December 23, 1996, Department of Computer Science, University College London.
S. B. Holden and M. Niranjan. On the Practical Applicability of VC Dimension Bounds. Neural Computation, Volume 7, Number 6, November 1995 pages 1265-1288.
S. B. Holden and M. Niranjan. On the Statistical Physics of Radial Basis Function Networks. Neural Processing Letters , Volume 2 Number 4, 1995, pages 16-19.
S. B. Holden and P. J. W. Rayner. Generalization and PAC Learning: Some New Results for the Class of Generalized Single Layer Networks. IEEE Transactions on Neural Networks, Volume 6, Number 2, March 1995, pages 368-380.
M. Anthony and S. B. Holden. Quantifying Generalization in Linearly Weighted Neural Networks. Complex Systems, Volume 8, pages 91-114, 1994.
S. B. Holden. Neural Networks and the VC Dimension. Proceedings of the Third IMA International Conference on Mathematics and Signal Processing , pages 73-84, Oxford University Press, 1994.
S. B. Holden. How Practical are VC Dimension Bounds. Proceedings of the IEEE International Conference on Neural Networks , 1994, volume 1, pages 327-332.
S. B. Holden. On the Theory of Generalization and Self-Structuring in Linearly Weighted Connectionist Networks. PhD Dissertation. Technical Report CUED/F-INFENG/TR.161, Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, U.K, 1994.
M. Anthony and S. B. Holden. On the Power of Linearly Weighted Neural Networks. Proceedings of the International Conference on Artificial Neural Networks (ICANN) 1993, Springer-Verlag, pages 738-743.
S. B. Holden. Valid Generalization in Radial Basis Function Networks and Modified Kanerva Models. Proceedings of the IEE Third International Conference on Artificial Neural Networks 1993, pages 100-104.
M. Anthony and S. B. Holden. On the Power of Polynomial Discriminators and Radial Basis Function Networks. Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory (COLT) 1993, pages 158-164.
S. B. Holden. High Order Filter Design and Implementation Project: Initial Report and Four Month Plan. King's College London, technical report number 108/SCS/93, March 5, 1993.
S. B. Holden. High Order Filter Design and Implementation Project: Summary of Present Position. King's College London, technical report number 108/SCS/93, March 30, 1993.
S. B. Holden and P. J. W. Rayner. Generalization and Learning in Volterra and Radial Basis Function Networks: A Theoretical Analysis. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing , 1992, volume 2, pages II-273-II-276.
M. R. Lynch, P. J. W. Rayner and S. B. Holden. Removal of Degeneracy in Adaptive Volterra Networks by Dynamic Structuring. Proceedings of the IEEE International Conference on Acoustics Speech and Signal Processing, 1991, volume 3, pages 2069-2072.
M. R. Lynch, S. B. Holden and P. J. W. Rayner. Complexity Reduction in Volterra Connectionist Networks using a Self-Structuring LMS Algorithm. Proceedings of the IEE Second International Conference on Artificial Neural Networks , 1991, pages 44-48.