A

Apeh, E. T., Gabrys, B. and Schierz, A. C., 2011. Customer profile classification using transactional data. In: Third World Congress on Nature and Biologically Inspired Computing (NaBIC), 19-21 Octpber 2011, Salamanca Spain. IEEE, pp. 37-43.

Atfield-Cutts, S., Davies, P., Rowe, N. and Newell, D., 2009. Multimedia Presentation System with Contextual Support BU & Bournemouth and Poole College. In: Education Enhancement Conference 2009, 21 May 2009, Bournemouth University.

Apeh, E. and Gabrys, B., 2006. Clustering for Data Matching. In: Gabrys, B., Howlett, R.J. and Jain, L.C., eds. Knowledge-Based Intelligent Information and Engineering Systems: 10th International Conference, KES 2006, Bournemouth, UK, October 9-11 2006. Berlin: Springer, pp. 1216-1225.

B

Budka, M., Gabrys, B. and Musial, K., 2011. On accuracy of PDF divergence estimators and their applicability to representative data sampling. Entropy, 13 (6), pp. 1229-1266.

Budka, M. and Gabrys, B., 2011. Electrostatic field framework for supervised and semi–supervised learning from incomplete data. Natural Computing, 10 (2), pp. 921-945.

Bose R. P., J. C., van der Aalst, W. M.P., Zliobaite, I. and Pechenizkiy, M., 2011. Handling concept drift in process mining. In: 23rd International Conference on Advanced Information Systems Engineering, June 20-24 2011, London, UK, pp. 391-405.

Barmpalexis, P., Kachrimanis, K., Tsakonas, A. and Georgarakis, E., 2011. Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation. Chemometrics and Intelligent Laboratory Systems, 107 (1), 75 - 82.

Budka, M. and Gabrys, B., 2010. Ridge regression ensemble for toxicity prediction. Procedia Computer Science, 1 (1), pp. 193-201.

Budka, M., Gabrys, B. and Ravagnan, E., 2010. Robust predictive modelling of water pollution using biomarker data. Water Research, 44 (10), pp. 3294-3308.

Budka, M. and Gabrys, B., 2010. Density Preserving Sampling (DPS) for error estimation and model selection. IEEE Transactions on Pattern Analysis and Machine Intelligence. (Unpublished)

Budka, M. and Gabrys, B., 2010. Correntropy–based density–preserving data sampling as an alternative to standard cross–validation. In: World Congress on Computational Intelligence (WCCI 2010), 18-23 July 2010, Barcelona, Spain, pp. 1-8.

Budka, M. and Gabrys, B., 2009. Electrostatic Field Classifier for Deficient Data. In: Kurzynski, M. and Wozniak, M., eds. Computer Recognition Systems 3. Heidelberg: Springer, pp. 311-318.

Bakker, J., Pechenizkiy, M., Zliobaite, I., Ivannikov , A. and Karkkainen, T, 2009. Handling Outliers and Concept Drift in Online Mass Flow Prediction in CFB Boilers. In: Third International Workshop on Knowledge Discovery from Sensor Data (SensorKDD’09), 28 June - 1 July, 2009., Paris, France, pp. 13-22.

Bouchachia, A., Gabrys, B. and Sahel, Z., 2007. Overview of Some Incremental Learning Algorithms. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'2007): Proceedings, 23-26 July 2007, London, pp. 1-6.

Baruque, B., Corchado, E., Gabrys, B., Herrero, Á., Rovira, J. and Gonzalez, J., 2006. Unsupervised Ensembles Techniques for Visualization. In: NiSIS'2006 Symposium : 2nd European Symposium on Nature-inspired Smart Information Systems, 29 November - 1 December 2006, Puerta de la Cruz, Tenerife, Spain.

Bobeva, M., 2005. A Framework for Information Architecture for Business Networks. PhD Thesis (PhD). Bournemouth University.

Burns, R.S., Richter, R. and Polkinghorne, M., 1995. A Multivariable neural network ship mathematical model. In: Graczyk, T., Jastrzebski, T., Brebbia, C.A. and Burns, R.S., eds. Proceedings of the International Conference on Marine Technology: Marine Technology and Transportation. WIT Press, pp. 747-756.

Burns, R.S. and Polkinghorne, M., 1993. A neural network controller for optimal guidance of surface ships. In: 10th Ship Control Systems Symposium, October 1993, Ottawa, Canada. WIT Press.

Browning, A. W., 1990. A Mathematical Model To Simulate Small Boat Behaviour. PhD Thesis (PhD). Bournemouth University.

C

Corchado, E., Baruque, B. and Gabrys, B., 2006. Maximum Likelihood Topology Preserving Ensembles. In: Corchado, E., Yin, H., Botti, V. and Fyfe, C., eds. Intelligent Data Engineering Andautomated Learning - Ideal 2006: 7th International Conference, Burgos, Spain, September 20-23, 2006. Springer Berlin / Heidelberg, pp. 1434-1442.

Cang, S., 2005. Novel Probability Neural Network. IEE Proceedings - Vision Image and Signal Processing, 152 (5), pp. 535-544.

Cang, S. and Partridge, D., 2004. Feature Ranking and Best Feature Subset Using Mutual Information. Neural Computing and Applications, 13 (3), pp. 175-184.

E

Eastwood, M. and Gabrys, B., 2009. A Non-Sequential Representation of Sequential Data for Churn Prediction. In: Knowledge-Based and Intelligent Information and Engineering Systems: 13th International Conference, KES 2009, Santiago, Chile, September 28-30, 2009, Proceedings, Part I. Heidelberg: Springer, pp. 209-218.

Engen, V., Vincent, J., Schierz, A. C. and Phalp, K. T., 2009. Multi-Objective Evolution Of The Pareto Optimal Set Of Neural Network Classifier Ensembles. In: International Conference of Machine Learning and Cybernetics (ICMLC & ICWAPR)2009., 12-15 July 2009, Baoding, Hebei, China, pp. 74-79.

Eastwood, M. and Gabrys, B., 2008. Building Combined Classifiers. In: Nguyen, N.T., Kolaczek, G. and Gabrys, B., eds. Knowledge Processing and Reasoning for Information Society. Warsaw, Poland: EXIT Publishing House, pp. 139-163.

Eastwood, M. and Gabrys, B., 2007. The Dynamics of Negative Correlation Learning. Journal of VLSI Signal Processing Systems, 49 (2), pp. 251-263.

Eastwood, M. and Gabrys, B., 2006. Lambda as a Complexity Control in Negative Correlation Learning. In: NiSIS'2006 Symposium : 2nd European Symposium on Nature-inspired Smart Information Systems, 29 November - 1 December 2006, Puerta de la Cruz, Tenerife, Spain.

Eves, B. and Lefley, M., 2000. The Aesthetic Concept Generator: Computing Aesthetic Semiotic Relations in Product Design. In: 4th International Conference on Adaptive Computing in Design and Manufacture, April 2000, University of Plymouth, England.

Eves, B., 1997. A Colour Aesthetic Mechanism Using 'Arty'-ficial Intelligence. In: 4th National Conference on Product Design Education, 7-8 July 1997, Brunel University, London.

Eves, B., 1997. The Colour concept generator: a computer tool to propose colour concepts for products. PhD Thesis (PhD). Bournemouth University.

Eves, B., 1996. Product Colour by Numbers. In: 3rd National Conference on Product Design Education , July 1996, University of Central Lancashire, England.

Eves, B., 1996. The Colour Concept Generator: 'Arty'-ficial Intelligence. In: SEED '96: 18th Annual Design Conference-CAD Education, June 1996, University of Bristol, England.

Eves, B., 1995. A 'Bit' of Colour Education in Product Design. In: 2nd National Conference on Product Design Education, July 1995, Coventry University, England.

Eves, B., 1994. The Colour Concept Generator: Computing the Aesthetics for Product Designers. In: 1st National Conference on Product Design Education, July 1994, Bournemouth University, England.

F

Fyfe, C. and Gabrys, B., 2002. Guest Editorial Introduction. Knowledge-Based Systems, 15 (1-2), pp. 1-2.

Fyfe, C. and Gabrys, B., 1999. E-insensitive Unsupervised Learning. In: International Conference on Neural Networks and Artificial Intelligence (ICNNAI'99), October 1999, Brest, Belarus, pp. 10-18.

G

Gabrys, B., 2009. Robust adaptive soft sensors for process industry - Keynote talk. In: International Workshop on Computational Intelligence in Security for Information Systems (CISIS'2009), 23-26 September 2009, Burgos, Spain. (Unpublished)

Gabrys, B., 2009. Learning with Missing or Incomplete Data. In: Image Analysis and Processing – ICIAP 2009. Springer Berlin / Heidelberg, pp. 1-4.

Gabrys, B. and Anguita, D., 2009. Nature-inspired Learning and Adaptive Systems. Natural Computing, 8 (2), pp. 197-198.

Gabrys, B., 2008. Self-adapting architecture for building powerful predictive models - Keynote talk. In: The 15th International Conference on Neural Information Processing (ICONIP 2008), 24-27 Nov 2008, Auckland, New Zealand. (Unpublished)

Gabrys, B., 2008. Do Smart Adaptive Systems Exist? Hybrid Intelligent Systems Perspective - Keynote Talk. In: Corchado, E., Abraham, A. and Pedrycz, W., eds. Hybrid Artificial Intelligence Systems: Third International Workshop, HAIS 2008, Burgos, Spain, September 24-26, 2008. Proceedings. Springer Berlin / Heidelberg, pp. 2-3.

Gabrys, B., Jones, M. and Polkinghorne, M., 2007. Commercial application of computational intelligence - a KTP case study. Technical Report. Bourenmouth, England, UK: Bournemouth University.

Gabrys, B., 2007. Keynote Speech. In: European Conference on Data Mining (ECDM'2007), 6-8 July 2007, Lisbon, Portugal. (Unpublished)

Gabrys, B., 2007. Keynote Speech. In: World Congress on Engineering (WCE'2007), 2-4 July 2007, London. (Unpublished)

Gabrys, B., 2006. Do Smart Adaptive Systems Exist? - Invited Lecture. In: Engineering Research in Action (ERA) Day, 13 June 2006, University of Brighton, England. (Unpublished)

Grewal, G. S., Bharara, M., Cobb, J. E., Dubey, V. N. and Claremont, D. J., 2006. A Novel Approach to Thermochromic Liquid Crystal Calibration Using Neural Networks. Measurement Science and Technology, 17, pp. 1918-1924.

Gabrys, B., 2006. Do Smart Adaptive Systems Exist? - Keynote Talk. In: Workshop on Adaptive Systems - Advanced Issues, 24 February 2006, Southampton, England. (Unpublished)

Gabrys, B. and Ruta, D., 2006. Genetic algorithms in classifier fusion. Applied Soft Computing, 6 (4), pp. 337-347.

Gabrys, B., 2006. Do Smart Adaptive Systems Exist? - A hybrid intelligent systems perspective. - Keynote Talk. In: 2006 International Forum on a Life and Adaptive Robotics, 8-9 November 2006, ITRC-Intelligent Robot Research Center at the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea. (Unpublished)

Gabrys, B., 2006. Do Smart Adaptive Systems Exist? - Soft Computing Perspective - Keynote Talk. In: 6th International Conference on Recent Advances in Soft Computing (RASC'2006), 10-12 July 2006, University of Kent, Canterbury, England. (Unpublished)

Gabrys, B., 2006. Do Smart Adaptive Systems Exist?- Hybrid Intelligent Systems Perspective - Invited Lecture. In: Summer School, July 2006, University of Burgos, Burgos, Spain. (Unpublished)

Gabrys, B., Baruque, B. and Corchado, E., 2006. Outlier Resistant PCA Ensembles. In: Gabrys, B., Howlett, R.J. and Jain, L.C., eds. Knowledge-Based Intelligent Information and Engineering Systems: 10th International Conference, Kes 2006, Bournemouth, UK, October 9-11 2006. Berlin: Springer, pp. 432-440.

Gabrys, B., 2006. To Combine or Not to Combine? - Multiple Classifier and Prediction Systems - Invited Lecture. In: Summer School, July 2006, University of Burgos, Burgos, Spain. (Unpublished)

Gunstone, R. E. and Lee, M.H., 2005. A Lens-Calibrated Active Marker Metrology System. In: TAROS 2005: The 2005 Towards Autonomous Robotic Systems Conference, 12-14 September, 2005, Imperial College, London, pp. 73-80.

Gabrys, B., 2005. Do smart adaptive systems exist? Introduction. In: Gabrys, B., Leiviskä, K. and Strackeljan, J., eds. Do Smart Adaptive Systems Exist? Best Practice for Selection and Combination of Intelligent Methods. Berlin: Springer, pp. 1-17.

Gabrys, B., 2005. Multilevel Classifier Systems - Issues, Motivations and Challenges - Invited Lecture. In: Talks at the Centre for Intelligent Agents and Multi-Agent Systems (CIAMAS), 5 September 2005, Swinburne University of Technology, Melbourne, Australia. (Unpublished)

Gabrys, B., 2005. Multilevel Classifier Systems - Issues, Motivations and Challenges - Invited Lecture. In: Talk at the Bioinformatics Applications Research Centre (BARC), 21 September 2005, James Cook University, Townsville, Australia. (Unpublished)

Gabrys, B., 2005. Multilevel Classifier Systems - Issues, Motivations and Challenges - Invited lecture. In: Monash Data Mining Centre (MDMC), 13 September 2005, Monash University, Melbourne, Australia. (Unpublished)

Gabrys, B., 2004. Learning hybrid neuro-fuzzy classifier models from data: to combine or not to combine? Fuzzy Sets and Systems, 147 (1), pp. 39-56.

Gabrys, B., 2004. Special Issue on Integration of Methods and Hybrid Systems. International Journal of Approximate Reasoning, 35 (3), pp. 203-204.

Gabrys, B., 2004. Hybrid Intelligent Methods and Smart Adaptive Systems - Keynote Talk. In: EUNITE'2004: European Network of Excellence on Intelligent Technologies for Smart Adaptive Systems Conference, 12-14 June 2004, Aachen, Germany. (Unpublished)

Gabrys, B. and Petrakieva, L., 2004. Selective sampling for combined learning from labelled and unlabelled data. In: Lotfi, A. and Garibaldi, J.M., eds. Applications and science in soft computing. London: Springer, pp. 139-148.

Gabrys, B. and Petrakieva, L., 2003. Combining labelled and unlabelled data in the design of pattern classification systems. International Journal of Approximate Reasoning, 35 (3), pp. 251-273.

Gabrys, B., 2002. Neuro-fuzzy approach to processing inputs with missing values in pattern recognition problems. International Journal of Approximate Reasoning, 30 (3), pp. 149-179.

Gabrys, B., 2002. Agglomerative learning algorithms for general fuzzy min-max neural network. Journal of VLSI Signal Processing Systems, 32 (1-2), pp. 67-82.

Gabrys, B., 2002. Combining Labelled and Unlabelled Data in the Design of Pattern Classification Systems. In: Hybrid Methods for Adaptive Systems (HMAS'2002) Workshop, 20 September 2002, Albufeira, Portugal.

Gabrys, B., 2002. Combining Neuro-Fuzzy Classifiers for Improved Generalisation and Reliability. In: Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on, May 12-17, 2002, Hilton Hawaiian Village Hotel, Honolulu, Hawaii, pp. 2410-2415.

Gabrys, B., 2001. Data Editing for Neuro-Fuzzy Classifiers. In: Fourth International ICSC Symposium: Proceedings of the SOCO/ISFI’2001 Conference, June 26 - 29, 2001, Paisley, Scotland, p. 77.

Gabrys, B., 2001. Data and Information Fusion. In: Invited Talk, Lufthansa Systems Berlin GmbH, 4 December 2001, Berlin, Germany. (Unpublished)

Gabrys, B., 2001. Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine? In: EUNITE'2001 Conference: European Symposium on Intelligent Technologies, Hybrid Systems and their Implementation on Smart Adaptive Systems, 13-14 December 2001, Puerto de la Cruz, Tenerife, Spain.

Gabrys, B., 2000. Agglomerative Learning for General Fuzzy Min-Max Neural Network. In: IEEE International Workshop on Neural Networks for Signal Processing, 11-13 December 2000, Sydney, Australia, pp. 692-701.

Gabrys, B., 2000. Pattern classification for incomplete data. In: Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on, 30 August -1 September 2000, Brighton, England, pp. 454-457.

Gabrys, B. and Bargiela, A., 2000. General fuzzy min-max neural network for clustering and classification. Neural Networks, IEEE Transactions on, 11 (3), pp. 769-783.

Gabrys, B. and Bargiela, A., 1999. Analysis of Uncertainties in Water Systems Using Neural Networks. Measurement + Control, 32 (5), pp. 145-147.

Gabrys, B. and Bargiela, A., 1999. Neural Networks Based Decision Support in Presence of Uncertainties. Journal of Water Resources Planning and Management, 125 (5), pp. 272-280.

Gabrys, B. and Bargiela, A., 1997. Integrated Neural Based System for State Estimation and Confidence Limit Analysis in Water Networks. In: The 8th European Simulation Symposium, Ess 96. Society for Computer Simulation, pp. 398-402.

Gabrys, B. and Bargiela, A., 1995. Neural Simulation of Water Systems for Efficient State Estimation. In: The European Simulation and Modelling Conference (ESM'95), June 5-7, 1995, Prague, Czech Republic, pp. 775-779.

H

Howlett, R.J., Lovrek, I., Jain, L.C., Lim , C.-P. and Gabrys, B., 2010. Advances in design and application of neural networks. Neural Computing and Applications (A special edition), 1 (2).

I

Isley, V. and Smith, P., 2009. Lost Calls of Cloud Mountain Whirligigs (view 1, left & right). Computer generated.Berlin, Germany: [DAM]Berlin.

Isley, V. and Smith, P., 2007. Ornamental Bug Garden 002. Screen based computational art work.Southampton, UK: boredomresearch.

Isley, V. and Smith, P., 2002. System 1.6 (Projection installation). Computational Artwork.Theatre of Restless Automata solo exhibition, Peterborough Digital Arts. FILE04, Electronic Language International Festival, Brazil. SoundToys Convergence exhibition, ICA, London. Tiny Beasts of Burden Open Hand Open Space solo exhibition, Reading. Data:B: UNSPECIFIED.

J

Juszczyszyn, K., Kazienko, P., Musial, K. and Gabrys, B., 2009. Temporal Changes in Local Topology of an Email-Based Social Network. Computing and Informatics, 28 (6), pp. 763-779.

K

Kadlec, P. and Gabrys, B., 2011. Local learning-based adaptive soft sensor for catalyst activation prediction. AIChE Journal, 57 (5), pp. 1288-1301.

Kadlec, P., Grbic, R. and Gabrys, B., 2011. Review of adaptation mechanisms for data-driven soft sensors. Computers and Chemical Engineering, 35 (1), pp. 1-24.

King, R. D., Schierz, A. C., Clare, A., Rowland, J., Sparkes, A., Nijssen, S. and Ramon, J., 2010. Inductive queries for a drug designing robot scientist. In: Dzeroski, S., Goethals, B. and Panov, P., eds. Inductive Databases and Constraint-Based Data Mining. Springer, pp. 421-451.

Kadlec, P. and Gabrys, B., 2010. Adaptive on-line prediction soft sensing without historical data. In: World Congress on Computational Intelligence (WCCI 2010), Barcelona, Spain.. (In Press)

Kadlec, P. and Gabrys, B., 2009. Architecture for development of adaptive on-line prediction models. Memetic Computing, 1 (4), pp. 241-269.

Kadlec, P. and Gabrys, B., 2009. Self-Adapting Soft Sensor for On-Line Prediction. In: Köppen, M., Kasabov, N. and Coghill, G., eds. Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part I. Heidelberg: Springer, pp. 1172-1179.

Kadlec, P., Gabrys, B. and Strandt, S., 2009. Data-driven Soft Sensors in the Process Industry. Computers and Chemical Engineering, 33 (4), pp. 795-814.

Kadlec, P. and Gabrys, B., 2009. Evolving on-line prediction model dealing with industrial data sets. In: 2009 IEEE Workshop on Evolving and Self-Developing Intelligent Systems Proceedings. Nashville: IEEE, pp. 24-31.

Kuncheva, L. and Zliobaite, I., 2009. On the Window Size for Classification in Changing Environments. Intelligent Data Analysis, 13 (6), pp. 861-872.

Kadlec, P. and Gabrys, B., 2008. Adaptive Local Learning Soft Sensor for Inferential Control Support. In: Mohammadian, M., ed. Computational Intelligence for Modelling Control & Automation, 2008 International Conference on. IEEE Computer Society, pp. 243-248.

Kadlec, P. and Gabrys, B., 2008. Application of Computational Intelligence Techniques to Process Industry Problems. In: Nguyen, N.T., Kolaczek, G. and Gabrys, B., eds. Knowledge Processing and Reasoning for Information Society. Warsaw, Poland: EXIT Publishing House, pp. 305-322.

Kadlec, P. and Gabrys, B., 2008. Gating Artificial Neural Network Based Soft Sensor. In: Nguyen, N. T. and Katarzyniak, R., eds. New Challenges in Applied Intelligence Technologies. Berlin: Springer-Verlag, pp. 193-202.

Kadlec, P. and Gabrys, B., 2008. Learnt Topology Gating Artificial Neural Networks. In: Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on. IEEE, pp. 2604-2611.

Kadlec, P. and Gabrys, B., 2007. Nature-Inspired Adaptive Architecture for Soft Sensor Modelling. In: NiSIS'2007 Symposium: 3rd European Symposium on Nature-inspired Smart Information Systems, 26- 27 November 2007, St Julian's, Malta.

L

Lemke, C., Riedel, S. and Gabrys, B., 2011. Evolving forecast combination structures for airline revenue management. Journal of Revenue and Pricing Management. (Submitted)

Lemke, C. and Gabrys, B., 2010. Meta-learning for time series forecasting and forecast combination. Neurocomputing, 73 (10-12), pp. 2006-2016.

Lemke, C. and Gabrys, B., 2010. Meta-learning for time series forecasting in the NN GC1 competition. In: World Congress on Computational Intelligence (WCCI 2010), Barcelona, Spain. (In Press)

Lemke, C., Riedel, S. and Gabrys, B., 2009. Dynamic Combination of Forecasts Generated by Diversification Procedures Applied to Forecasting of Airline Cancellations. In: 2009 IEEE Symposium on Computational Intelligence for Financial Engineering Proceedings. Nashville: IEEE, pp. 85-91.

Lemke, C. and Gabrys, B., 2008. Do We Need Experts for Time Series Forecasting? In: 16th European Symposium on Artificial Neural Networks (ESANN'2008), April 2008, Bruges, Belgium, pp. 253-258.

Lemke, C. and Gabrys, B., 2008. Forecasting and Forecast Combination in Airline Revenue Management Applications. In: Nguyen, N.T., Kolaczek, G. and Gabrys, B., eds. Knowledge Processing and Reasoning for Information Society. Warsaw, Poland: EXIT Publishing House, pp. 231-247.

Lemke, C. and Gabrys, B., 2008. On the Benefit of Using Time Series Features for Choosing a Forecasting Method. In: 2nd European Symposium on Time Series Prediction, 17-19 Sep 2008, Porvoo, Finland.

Liu, H., Howlett, R.J. and Gabrys, B., 2007. Special Issue: Extended Papers Selected from KES-2006. International Journal of Knowledge-Based Intelligent Engineering Systems, 11 (4), pp. 199-200.

Lemke, C. and Gabrys, B., 2007. Review of Nature-Inspired Forecast Combination Techniques. In: NiSIS'2007 Symposium: 3rd European Symposium on Nature-inspired Smart Information Systems, 26- 27 November 2007, St Julian's, Malta.

Leavey, C., Polkinghorne, M., Sutton, R. and Burrows, R.J., 1999. An intelligent decision support system for specialist product design. In: Roberts, G.N. and Tubb, C.A.J., eds. Proceedings of WESIC '99 - Workshop on European Scientific and Industrial Collaboration: Promoting Advanced Technologies in Manufacturing. Mechatronics Research Centre, University of Wales College Newport, pp. 317-326.

M

Musial, K., Budka, M. and Blysz, W., 2012. Understanding the other side – the inside story of the INFER Project. In: Innovation Through Knowledge Transfer 2012 - InnovationKT'12, 19-20 Apr 2012, Bournemouth, England. (Unpublished)

Mazhelis, O., Zliobaite, I. and Pechenizkiy, M., 2011. Context-aware personal route recognition. In: The Fourteenth International Conference on Discovery Science (DS 2011), Espoo, Finland, pp. 365-379.

Macas, M.B., Gabrys, B., Ruta, D. and Lhotska, L., 2007. Particle Swarm Optimisation of Multiple Classifier Systems. In: Sandoval, F., ed. Computational and Ambient Intelligence: 9th International Work-Conference on Artificial Neural Networks, IWANN 2007, San Sebastian, Spain, June 20-22. Berlin: Springer, pp. 333-340.

P

Pechenizkiy, M., Vasilyeva, E., Zliobaite, I., Tesanovic, A. and Manev, G., 2010. Heart Failure Hospitalization Prediction in Remote Patient Management Systems. In: IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS '10), 12-15 October 2010, Perth, Australia, pp. 44-49.

Pampanin, D.M., Ravagnan, E., Apeland, S., Aarab, N., Godal, B.F., Westerlund, S., Hjermann, D.Ø., Budka, M., Gabrys, B., Viarengo, A. and Barsiene, J., 2010. The Marine Environment I.Q. concept. Developing an Index of the Quality of the Marine Environment based on biomarkers: integration of pollutant effects on marine organisms. Comparative Biochemistry and Physiology - Part A: Molecular & Integrative Physiology, S52.

Pechenizkiy, M., Bakker, J., Zliobaite, I., Ivannikov , A. and Karkkainen, T, 2009. Online Mass Flow Prediction in CFB Boilers with Explicit Detection of Sudden Concept Drift. ACM SIGKDD explorations newsletter, 11 (2), pp. 109-116.

Polkinghorne, M., Roberts, G.N. and Burns, R.S., 1997. Intelligent ship control with online learning ability. Computing and Control Engineering Journal, 8 (5), pp. 196-200.

Polkinghorne, M., Roberts, G.N. and Burns, R.S., 1997. Consideration of performance assessment criteria required for a self-organising fuzzy logic autopilot. In: 11th Ship Control Systems Symposium: Transport and Transformation of Pollutants in the Troposphere Vol 1. WIT Press, pp. 151-159.

Polkinghorne, M., 1997. Editorial - special section on marine control. Control theory and applications, IEE Proceedings D, 144 (2), p. 109.

Polkinghorne, M., 1997. Understanding simple neural networks. Plymouth, England: University of Plymouth.

Polkinghorne, M., Roberts, G.N. and Burns, R.S., 1996. Practical results from full scale sea trials for a self-organising design of fuzzy autopilot. In: IEE Colloquium on Artificial Intelligence in Consumer and Domestic Products, 22 October 1996, London. IEEE, 7/1-7/5.

Polkinghorne, M., 1996. Fuzzy logic autopilot for small boats. Electrotechnology, 7 (3), pp. 14-16.

Polkinghorne, M., Burns, R.S. and Roberts, G.N., 1996. Operational performance of an initial design of self-organising fuzzy logic autopilot. In: IEE International Conference on Control Engineering, 1996, Exeter, 1:586-1:591.

Polkinghorne, M., 1996. Self-organising fuzzy logic for control: intelligent autopilot case study. Plymouth, England: University of Plymouth.

Polkinghorne, M. and Pakravan, S., 1995. BBC TV Tomorrow's World, Fuzzy logic autopilot for boats. Television. London: UNSPECIFIED. October 1995.

Polkinghorne, M. and Burns, R.S., 1995. Control with intelligence. Offshore Research Focus, 106 (4).

Polkinghorne, M., Burns, R.S. and Roberts, G.N., 1995. Full-scale sea trial results of a self-organising fuzzy logic autopilot for small vessels. In: Proceedings of the Symposium on the Application of Advanced Marine Control, Plymouth, 1995. University of Plymouth, England, pp. 18-27.

Polkinghorne, M., Burns, R.S. and Roberts, G.N., 1995. Performance index derivation for a self-organising fuzzy autopilot. In: Graczyk, T., Jastrzebski, T., Brebbia, C.A. and Burns, R.S., eds. Proceedings of the International Conference on Marine Technology: Marine Technology and Transportation. WIT Press, pp. 757-766.

Polkinghorne, M. and Burns, R.S., 1994. Fuzzy logic autopilot. Offshore Research Focus, 102 (9).

Polkinghorne, M., Roberts, G.N. and Burns, R.S., 1994. The implementation of a fuzzy logic marine autopilot. In: IEEE International Conference on Control 1994 Proceedings. IEEE, pp. 1572-1577.

Polkinghorne, M., Roberts, G.N., Burns, R.S. and Winwood, D., 1994. The implementation of fixed rulebase fuzzy logic to the control of small surface ships. International federation of automatic control (IFAC). Control Engineering Practice, 3 (3), pp. 321-328.

Polkinghorne, M., Roberts, G.N. and Burns, R.S., 1993. Small marine vessel application of a fuzzy PID autopilot. In: 12th IFAC World Congress Proceedings, Sydney, Australia, 19-23 July 1993. IFAC, pp. 409-412.

Polkinghorne, M., Burns, R.S. and Roberts, G.N., 1992. A fuzzy autopilot for small vessels. In: Wilson, P.A., ed. Manoeuvring and Control of Marine Craft: 2nd International Conference Proceedings. WIT Press, pp. 349-362.

Polkinghorne, M., Burns, R.S., Randolph, W. and Roberts, G.N., 1991. A review of autopilots and associated control simulation techniques. In: Modelling and Simulation, 1991: Proceedings of the 1991 European Simulation Multiconference, June 17-19, 1991 the Panum Institute Copenhagen, Denmark. Society for Computer Simulation.

R

Rowe, N., Atfield-Cutts, S., Davies, P. and Newell, D., 2010. Implementation and Evaluation of an Adaptive Multimedia Presentation System (AMPS) with Contextual Supplemental Support Media. In: MMEDIA 2010: The Second International Conferences on Advances in Multimedia, 13-19 June 2010, Athens/Glyfada, Greece. (Submitted)

Ruta, D., Gabrys, B. and Lemke, C., 2010. A Generic Multilevel Architecture for Time Series Prediction. IEEE Transactions on Knowledge and Data Engineering, 99.

Riedel, S. and Gabrys, B., 2009. Pooling for Combination of Multi Level Forecasts. IEEE Transactions on Knowledge and Data Engineering, 21 (12), pp. 1753-1766.

Ruta, D. and Gabrys, B., 2009. A Framework for Machine Learning Based on Dynamic Physical Fields. Natural Computing, 8 (2), pp. 219-237.

Riedel, S., 2008. Forecast combination in revenue management demand forecasting. PhD Thesis (PhD). Bournemouth University.

Riedel, S. and Gabrys, B., 2007. Combination of Multi Level Forecasts. Journal of VLSI Signal Processing Systems, 49 (2), pp. 265-280.

Ruta, D. and Gabrys, B., 2007. Reducing Spatial Data Complexity for Classification Models. In: Maroulis, G. and Simos, T.E., eds. Computational Methods in Science and Engineering: Theory and Computation: Old Problems and New Challenges (AIP Conference Proceedings). Melville, N.Y.: American Institute of Physics, pp. 603-613.

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