Alex Gammerman - Selected Bibliography

Books

A. Gammerman, (ed.) Probabilistic Reasoning and Bayesian Belief Networks. Alfred Waller, Henley-on-Thames, 1995.

A. Gammerman, (ed.) Computational Learning and Probabilistic Reasoning. John Wiley & Sons, Chichester, 1996.

A. Gammerman. Machine Learning: Progress and Prospects. ISBN 0 900145 93 5, 1997 (Postscript)

A. Gammerman, (ed.) Causal Models and Intelligent Data Managment. Springer-Verlag, 1999.

V. Vovk, A. Gammerman and G. Shafer. Algorithmic Learning in a random world. Springer, 2005.

A.Gammerman, (ed.) Artificial Intelligence and Applications, Proceedings of the Conference, ACTA Press, ISBN: 978-0-88986-709-3, 2008.

Special Issues

A. Gammerman and V. Vovk, (editors). Kolmogorov Complexity. Special Issue of The Computer Journal, vol. 42, No. 4, pp.251-347, 1999.

C. Aitken, T. Connolly, A. Gammerman, G. Zhang, D. Oldfield. Predicting an Offender's Characteristics: an evaluation of statistical modelling Special Interest Series - Paper 4, Home Office, London, 1995.

Selected Book Chapters, Journal Papers, Conference Proceedings

If you would like a copy of any of these articles, please contact me.

Clinical Mass Spectrometry Proteomic Diagnosis by Conformal Predictors. Accepted for publication in Statistical Applications in Genetics and Molecular Biology Journal (with Ilia Nouretdinov, Brian Burford Alexey Chervonenkis, Vladimir Vovk and Zhiyuan Luo).

Predicting Clinical Outcome in Patients Diagnosed with Synchronous Ovarian and Endometrial Cancer Accepted for publication in Clinical Cancer Research (with Susan Ramus, Karim Elmasry, Zhiyuan Luo, Karen Lu, Ayse Ayhan, Naveena Singh, WG McCluggage, Ian Jacobs, John Whittaker, Simon Gayther).

Cancer informatics by prototype networks in mass spectrometry. Accepted for publication in the special issue of Artificial Intelligence in Medicine (with ~F-~M.~Schleif, T.~Willmann, M.~Kostrzewa, B.~Hammer).

Hedging Predictions in Machine Learning. The Computer Journal, v.50, No.2, 151-163, March 2007 (with V. Vovk). The same journal also published:
i) Discussion on Hedging Predictions in Machine Learning by A. Gammerman and V. Vovk. The Computer Journal, 2007, 50: 164-172;
ii) Rejoinder Hedging Predictions in Machine Learning. The Computer Journal, 2007, 50: 173-177.

Adaptive Coding and Prediction of Sources with Large and Infinite Alpha- bets. IEEE Transactions on Information Theory, accepted for publication; (with B.Ryabko and J.Astola).

Ovarian Cancer Serum Biomarker Discovery Using MALDI-TOF MS Proteomic Profiling, In Proceedings of the 15th International Meeting of the European Society of Gynaecological Oncology, Berlin, Germany, 2007 (with S. Camuzeaux, M. Kabir, B. Burford, I. Nouretdinov, V. Vovk, J. Ford, Z. Luo, A. Gentry-Maharaj, M. Waterfield, U. Menon, J.F. Timms and I. Jacobs).

Validation of a Seven Serum Biomarker Panel in Ovarian Cancer, In Proceedings of the 15th International Meeting of the European Society of Gynaecological Oncology, Berlin, Germany, 2007 (with J.F. Timms, E. Arsian-Low, M. Cubizolles, L. Lomas, C. Yip, X-Y Meng, E. Hogdall. C. Hogdall, A. Gentry-Maharaj, J.J. Ford, B. Burford, I. Nouretdinov, V. Vovk, Z. Luo, U. Menon, E.T. Fung and I. Jacobs).

Improving the Aggregating Algorithm for Regression. Artificial Intelligence and Applications, In Proceedings of the 25th IASTED Conference Artificial Intelligence and Applications (AIA 2007), pp.347-352, Innsbruck, Austria, (2007), Editor: V.Devedzic (with S.Busutill and Y.Kalnishkan).

Normalized Nonconformity Measures for Regression Conformal Prediction. Artificial Intelligence and Applications . AIA 2008 Conference, Innsbruck, Austria, pp.64-69, (with H. Papadopoulos and V. Vovk).

Preanalytic Influence of Sample Handling on SELDI-TOF Serum Protein Profiles. Clinical Chemistry, 53, 645-656, April 2007, (with John F. Timms, Elif Arslan-Low, Aleksandra Gentry-Maharaj, Zhiyuan Luo, Davy T.Jampens, Vladimir N. Podust, Jeremy Ford, Eric T. Fung, Ian Jacobs, and Usha Menon).

Conformal Prediction with Neural Networks. 19th Annual IEEE International Conference on Tools with Artificial Intelligence, Patras, Greece, 2007 (with H. Papadopoulos and V. Vovk).

Compact Descriptors for Automatic Target Identification. Electromagnetic Remote Sensing Defense Technology Centre Technical Conference, Edinburgh, UK, 2007 (with A. Chervonenkis, I. Nouretdinov, J. Nothard and K. Smart).

Application of Kolmogorov complexity and universal codes to identity testing and nonparametric testing of serial independence for time series. Theoretical Computer Science, v.359, No.1-3, August 2006; also in e-print archive, 2005, http://arxiv.org/abs/cs/0505079 (with B.Ryabko and J.Astola).

Transductive Learning. Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on Advanced Intelligent Systems, CD, Tokyo, Japan, 2006.

Reliable classification of childhood acute leukaemia from gene expression data using Confidence Machines. IEEE International Conference on Granular Computing, Atlanta, USA, 2006 (with Zhiyuan Luo and Anthony Bellotti).

Qualified Probabilistic Predictions using Graphical Models, In L. Godo (eds) ECSQARU 2005, Lecture Notes in Artificial Intelligence 3571, pp. 111-122, Springer-Verlag Berlin Heidelberg, August 2005 (with Z. Luo).

Plant Promoter Prediction with Confidence Estimation. Nucleic Acids Research, 33(3), pp. 1069-1076, 2005 (with I. Shahmuradov and V.V. Solovyev).

Qualified Predictions for Proteomics Pattern Diagnostics with Confidence Machines. In: Intelligent Data Engineering and Automated Learning - IDEAL 2004 Lecture Notes in Computer Science 3177, pp 46-51, Springer, 2004 (with Zhiyuan Luo and Tony Bellotti).

On-line Predictions with Kernels and the Complexity Approximation Principle. Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI - 04), 2004, pp. 170-176, AUAI Press (with Yuri Kalnishkan, and Vladimir Vovk).

Testing exchangability on-line. Proceedings of the 20th International Conference on Machine Learning (ed. by T. Fawcett and N. Mishra), 2003, pp. 768-775, Menlo Park, CA, AAAJ Press (with I. Nouretdinov and V. Vovk).

Sequence alignement kernel for recognition of promoter regions. Bioinformatics, 19, 2003, 1964-1971 (with Gordon L., Chervonenkis A.Ya., Shahmuradov I.A. and Solovyev V.V.).

Genome-wide prokaryotic recognition based on sequence alignment kernel. Advances in Intelligent Data Analysis, (ed. by Berthold, Lenz, Bradley, Kruse and Borgelt), 2003, pp.386-396, Springer Verlag (with L. Gordon, Chervonenkis A.Ya. and Shahmuradov I.A.).

Prediction algorithms and confidence measures based on algorithmic randomness theory, Theoretical Computer Science, 287 (2002) 209-217 (with V. Vovk).

Transductive Confidence Machines for Pattern Recognition, European Conference on Machine Learning, Lecture Notes in Artificial Intelligence, pp. 381-390, 2002 (with Kostas Proedrou, Ilia Nouretdinov and Volodya Vovk).

Inductive Confidence Machines for Regression, European Conference on Machine Learning, Lecture Notes in Artificial Intelligence, pp.345-356, 2002 (with Harris Papadopoulos, Kostas Proedrou and Volodya Vovk).

A combined Bayes-maximum likelihood method for regression. Data Fusion and Perception, Riccia, Lenz, Kruse eds, Springer-Verlag Wein New York, 2001 (with A. Chervonenkis and M. Herbster).

Pattern Recognition and density estimation under the general i.i.d. assumption. Proceedings of Computational Learning Theory (COLT), Amsterdam, 2001 (with I. Nouretdinov, M. V'yugin and V. Vovk).

Support Vector Machine Learning Algorithm and Transduction. In: Computational Statistics, v.15, pp.31-39, 2000.

Application of Support Vector Machines to Fault Diagnosis, In: Proceedings of the Eleventh International Workshop on the Principles of Design (DX'00), 2000 (with C. Saunders, H. Brown, and G. Donald).

Computationally Efficient Transductive Machines, In: Proceedings of the Eleventh International Conference on Algorithmic Information Theory (ALT 2000), Lecture Notes in Computer Science, Springer-Verlag, pp.325-333, 2000 (with C. Saunders and V. Vovk).

Preoperative Differentiation of Ovarian Tumors using Support Vector Machine and Risk Malignancy Index, In: Proceedings of the International Federation of Obstetrics and Gynaecology (FIGO) Conference, Washington, 2000 (with P. van Trappen, M. Stitson, R. Wools, S. Barnhill, V. Vapnik and I. Jacobs.

Statistical applications of algorithmic randomness. In: International Statistics institute, 52nd Session, Helsinki, 1999 (with V.Vovk).

Machine Learning Applications of Algorithmic Randomness. In: Machine Learning, Proceedings of the Sixteen International Conference (ICML'99), 1999 (with V.Vovk and C.Saunders). (Postscript)

Transduction with Confidence and Credibility. In Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 1999 (with V.Vovk and C.Saunders). (Postscript)

Support Vector Regression with ANOVA Decomposition Kernels, In Scholkopf B., Burges C.J.C., and Smola A.J., editors, Advances in Kernel Methods, Support Vector Learning, pages 285-291. The MIT Press, Cambridge, Mass and London, England, 1999 (with M. Stitson, V. Vapnik, V. Vovk, C. Watkins and J. Weston).

Support Vector Density Estimation. In Scholkopf B., Burges C.J.C., and Smola A.J., editors, Advances in Kernel Methods, Support Vector Learning, pages 293-305. The MIT Press, Cambridge, Mass and London, England, 1999 (with J. Weston, M. Stitson, V. Vapnik, V. Vovk and C. Watkins).

Kolmogorov Complexity: Sources, Theory and Applications. The Special Issue of The Computer Journal, v.42, No.4, 1999 (with V.Vovk).

Predictive Complexity Principle. The Special Issue of The Computer Journal, v.42, No.4, 1999 (with V. Vovk).

Learning Algorithms in High Dimensional Space. In: Causal Models and Intelligent Data Managment, Springer, 1999 (with V. Vovk).

A combined Bayesian - ML approach to model selection, in Proceedings of IJCAI99 Workshop on Support Vector Machine, Stockholm, Sweden, 1999 (in press) (with A.Chervonenkis and M.Herbster).

Learning by Transduction. In Cooper G.F. and Moral S., editors, Uncertainty in Artificial Intelligence, Procs of the Fourteenth Conference (1998), Madison, Wisconsin, July 1998, pages 148-155. Morgan Kaufmann, San Francisco, CA, 1998. (with V. Vovk and V. Vapnik). (Postscript)

Learning by Support Vector Machine, Ridge Regression and Transduction. In: NTTS98: International Conference on New Techiques and Technologies for Statistics; pp.175-181, Sorrento, Italy, 1998.

Ridge Regression Learning Algorithm in Dual Variables, Proceedings of the 15th International Conference on Machine Learning, 1998 (with C. Saunders and V. Vovk). (Postscript)

Statistical modelling in specific case analysis. Science and Justice, 36(4):245-255, 1996. (with C.G.G.Aitken, T.Connolly, G.Zhang, D.B.Bailey, R.Gordon and R.Oldfield)

Bayesian belief networks with an application in specific case analysis. In A. Gammerman, editor, Computational Learning and Probabilistic Reasoning, pages 169-184. John Wiley & Sons, Chichester, 1996. (with C.G.G.Aitken, G.Zhang, T.Connolly, D.B.Bailey, R.Gordon and R.Oldfield).

Exact and approximate algorithms and their implementations in mixed graphical models. In A. Gammerman, editor, Probabilistic Reasoning and Bayesian Belief Networks, pages 33-53. Alfred Waller, Henley-on-Thames, 1995 (with Z.Luo, C.G.G.Aitken and M.Brewer).

Induction experiments with a minimal length encoding system. In UNICOM Seminar on Applied Decision Technologies, pages 209-222, London, 1995 (with A.Bellotti).

Using multiple chains for Gibbs sampling in mixed graphical association models. In Computational Statistics - COMPSTAT, pages 185-189, Physica-Verlag, Heidelberg, Germany, 1994 (with M. Brewer and Z. Luo).

Computational models of probabilistic reasoning. In D.J. Hand, editor, AI and Computer Power, The impact of statistics, pages 149-168. Chapman and Hall, 1994.

Geometric analogy problem by minimal-length encoding. In International Federation of Classification Societies Conference, IFCS, pages 201-203, Paris, 1993.

A connectionist expert system and its application to a large set of medical data. In Medical Informatics Europe MIE-93, Jerusalem, February 1993 (with H. Styri).

Stochastic simulation in mixed graphical association models. In K. Dodge and F. Whittaker, editors, Computational Statistics, volume 1, pages 257-262, 1992 (with C.G.G.Aitken, M.J.Brewer and Z.Luo).

Machine learning algorithms. In IMA Journal of Mathematics Applied in Business and Industry, 3(3), 1992 (with R.H.Davis and D.B.Edelman).

The representation and manipulation of the algorithmic probability measure for problem solving. Annals of Mathematics and Artificial Intelligence, 4:281-300, 1991.

Bayesian diagnostic probabilities without assuming independence of symptoms. Methods of Information in Medicine, 30(1):44-52, 1991 (with A.R.Thatcher).

PRESS - a probabilistic reasoning expert system shell. Number 548 in Lecture Notes in Computer Science, pages 232-237. Springer-Verlag, 1991 (with Z. Luo).

STOSS - a stochastic simulation system for bayesian belief networks. Number 521 in Lecture Notes in Computer Science, pages 97-105. Springer-Verlag, 1991.

An intelligent tutoring system for medical students. Theoretical Surgery, 5(3), 1990 (with D.Wang).

A computer-aided medical system and its application to the diagnosis of abdominal pain. Theoretical Surgery, 5(3), 1990 (with Y.Gu).

A causal probabilistic reasoning system. In 4th International Symposium on Knowledge Engineering, pages 23-41, Barcelona, May 1990.

Bayesian inference in an expert system without assuming independence. In M. Golumbic, editor, Advances in Artificial Intelligence, Natural Languages and Knowledge Based Systems, pages 182-218. Springer-Verlag, 1990 (with A.R.Thatcher).

Probabilistic reasoning in evidential assessment. Journal of the Forensic Science Society, 29(5):1-13, 1989 (with C.Aitken).

A hybrid approach to deductive uncertain inference. International Journal Man-Machine Study, 28:671-681, 1988 (with X.Liu).

An application of expert systems technology to identification task. Taxon, 36(4):705-714, 1987 (with W.Atkinson).

On the validity and applicability of the INFERNO system. In Research and Development in Expert Systems III, pages 47-56. Cambridge University Press, 1987 (with X.Liu).

Modelling of uncertainty in expert systems. In Procs of the 2nd International Conference on Expert Systems, London, pages 132-141, 1986.

An expert system for biological identification. In SPIE, Applications of Artificial Intelligence IV, volume 657, pages 34-38, Washington, 1986 (with B.Skullerand and W.Atkinson).

Patents

Data classification apparatus and method thereof. European Patent Application No. 99 954 200.4: the application was allowed in July 2004. US Patent Application No. 09/831,262: allowed.

Data labelling apparatus and method thereof. UK Patent Application GB 0017740.2: pending.

Technical Reports (Selection)

The technical reports listed here are those which (a) have not been published otherwise, or (b) have been published otherwise in a significantly shorter form, or (c) are more easily accessible as technical reports.

Theory of SV machines (joint work with M. O. Stitson, J.Weston, V. Vovk and V. Vapnik). Technical Report CSD-TR-96-17, Department of Computer Science, Royal Holloway, University of London, December 1996.

Support Vector ANOVA decomposition (joint work with M. O. Stitson, A. Gammerman, V. Vapnik, C. Watkins and J. Weston). Technical Report CSD-TR-97-22, Department of Computer Science, Royal Holloway, University of London, November 1997.

Density estimation using support vector machines (joint work with J. Weston, V. Vovk, M. O. Stitson, V. Vapnik and C. Watkins). Technical Report CSD-TR-97-23. Department of Computer Science, Royal Holloway, University of London, November 1997, revised February 1998.

Support Vector Machine: Reference Manual (joint work with C. Saunders, M. O. Stitson, J. Weston, V. Vovk, and C. Watkins). Technical Report CSD-TR-98-03, Department of Computer Science, Royal Holloway, University of London, April 1998.

Complexity Approximation Principle (joint work with V. Vovk). Technical Report CSD-TR-99-05, Department of Computer Science, Royal Holloway, University of London, January 1999.

Transductive Confidence Machines for pattern recognition (joint work with K. Proedrou, I. Nouretdinov and V. Vovk). Technical Report CLRC-TR- 01-02, Computer Learning Research Centre, Royal Holloway, University of London, June 2001.

Pattern recognition and density estimation under the general i.i.d. assumption (joint work with I. Nouretdinov, M. Vyugin and V. Vovk). Technical Report CLRC-TR-01-06, Computer Learning Research Centre, Royal Holloway, University of London, June 2001.

On-line Confidence Machines are well-calibrated. Technical Report CLRC-TR-02-01, Computer Learning Research Centre, Royal Holloway, University of London, April 2002.

Mondrian Confidence Machine (joint work with D. Lindsay, I. Nouretdinov and V. Vovk). On-line Compression Modelling project, Working Paper #4, 2003.

Online region prediction with real teachers, (joint work with D. Ryabko and V. Vovk). On-line Compression Modelling project, Working Paper #7, 2003.

Mass Spectrometry Data Analysis: Preprocessing and Pattern Recognition of the Sloan-Kettering Data. CLRC Technical Report 01-02-2005; (joint work with I.Nouretdinov, Z.Luo, A.Chervonenkis, V.Vovk Paul Tempst, John Philip, Josep Villanueva). 2004-2005.

Data Analysis of Human Serum Proteome II:UKCTOCS Data Pilot Study. (joint work with Ilia Nouretdinov, Brian Burford, Zhiyuan Luo, Alexey Chervonenkis, Volodya Vovk, John Timms, Mike Waterfield, Musarat Kabir, Paul Tempst, Josef Villanueva, Usha Menon and Ian Jacobs). November, 2005.

Two New Kernel Least Squares Based Methods for Regression, (joint work with S. Busuttil and Y. Kalnishkan), March 2006.

Data Analysis I - Comparison of Protocols, Version 2; (joint work with Ilia Nouretdinov, Brian Burford, Zhiyuan Luo, Alexey Chervonenkis and Volodya Vovk), June 2006.

Data Analysis II: Comparison of Plasma Protocols (joint work with Ilia Nouretdinov, Brian Burford, Zhiyuan Luo, Alexey Chervonenkis, Volodya Vovk, Davy T'Jampens, Eric T. Fung, Elif Arslan-Low, Jeremy Ford, Aleksandra Gentry-Maharaj, John Timms, Adam Rosenthal, Usha Menon and Ian Jacobs), 2006.

Serum proteomic abnormality predating screen detection of ovarian cancer. (joint work with Ilia Nouretdinov, Brian Burford, Zhiyuan Luo, Alexey Chervonenkis, Volodya Vovk, Musarat Kabir, John Timms, Paul Tempst, Josef Villanueva, Usha Menon and Ian Jacobs), 2007.