Items where Subject is "Generalities > Computer Science and Informatics > Artificial Intelligence"

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Number of items at this level: 76.

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.

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., 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 (Unpublished)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: ICJNN 2010: International Joint Conference on Neural Networks. IEEE, pp. 1-8.

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.

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.

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

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.

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

F

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

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. 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., 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. 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.

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.

K

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

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.

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.

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)

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

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.

Riedel, S. and Gabrys, B., 2007. Dynamic Pooling for the Combination of Forecasts Generated Using Multi Level Learning. In: Neural Networks, 2007. IJCNN 2007. International Joint Conference on, 12-17 Aug. 2007, Orlando, FL,, pp. 454-459.

Ruta, D. and Gabrys, B., 2007. Neural Network Ensembles for Time Series Prediction. In: Neural Networks, 2007. IJCNN 2007. International Joint Conference on, 12-17 Aug. 2007, Orlando, FL,, pp. 1204-1209.

Riedel, S. and Gabrys, B., 2005. Evolving Multilevel Forecast Combination Models - An Experimental Study. In: NiSIS'2005 (Nature-Inspired Smart information Systems) Symposium, 4- 5 October 2005, Albufeira, Portugal.

Ruta, D. and Gabrys, B., 2005. Nature-Inspired Learning Models. In: NiSIS'2005 (Nature-Inspired Smart Information Systems) Symposium, 4 - 5 October 2005, Albufeira, Portugal.

Riedel, S. and Gabrys, B., 2003. Adaptive Mechanisms in an Airline Ticket Demand Forecasting System. In: EUNITE'2003 Conference: European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems, 10 - 12 July 2003, Oulu, Finland.

Ruta, D. and Gabrys, B., 2003. Set analysis of coincident errors and its applications for combining classifiers. In: Chen, D. and Cheng, X., eds. Pattern Recognition and String Matching. Dordrecht; Boston: Kluwer Academic, pp. 647-672.

Ruta, D. and Gabrys, B., 2001. Analysis of the Correlation Between Majority Voting Error and the Diversity Measures in Multiple Classifier Systems. In: Soft Computing and Intelligent Systems for Industry: Proceedings and Scientific Program : Fourth International ICSC Symposium 2001, 26-29 June, 2001, Paisley, Scotland, p. 50.

Rogers, P. A. P., 2000. The Baby project: processing character patterns in textual representations of language. PhD Thesis (PhD). Bournemouth University.

Ruta, D. and Gabrys, B., 2000. An Overview of Classifier Fusion Methods. Computing and Information Systems, 7 (1), pp. 1-10.

S

Stahl, F. and Jordanov, I., 2012. An Overview of the Use of Neural Networks for Data Mining Tasks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2 (3), pp. 193-208.

Schierz, A. C., Budka, M. and Apeh, E., 2011. Winners’ notes. Using Multi-Resolution Clustering for Music Genre Identification. TunedIT.

Smith, P. and Isley, V., 2010. Lost Calls of Cloud Mountain Whirligigs (view 2, left & right). Computer generated.Istanbul, Turkey: UNSPECIFIED.

Schierz, A. C., 2009. Virtual Screening of Bioassay Data. Journal of Cheminformatics, 1 (21).

T

Tsakonas, A. and Gabrys, B., 2011. Evolving Takagi-Sugeno-Kang fuzzy systems using multi-population grammar guided genetic programming. In: International Conference on Evolutionary Computation Theory and Applications (ECTA'11), 24-26 Oct 2011, Paris, France.

Tong, D. L., Phalp, K. T., Schierz, A. C. and Mintram, R., 2009. Innovative Hybridisation of Genetic Algorithms and Neural Networks in Detecting Marker Genes for Leukaemia Cancer. In: 4th IAPR International Conference in Pattern Recognition for Bioinformatics, 7-9 September 2009, Sheffield, UK.

Tsakonas, A. and Dounias, G., 2008. Predicting Defects in Software Using Grammar-Guided Genetic Programming. In: Darzentas, J., Vouros , G., Vosinakis, S. and Arnellos, A., eds. Artificial Intelligence: Theories, Models and Applications: 5th Hellenic Conference on AI, SETN 2008, Syros, Greece, October 2-4, 2008, Proceedings. Berlin-Heidelberg: Springer-Verlag, pp. 413-418.

Tsakonas, A. and Dounias, G., 2007. Evolving Neural-Symbolic Systems Guided by Adaptive Training Schemes: Applications in Finance. Applied Artificial Intelligence, 21 (7), pp. 681-706.

Tsakonas, A., Nikolaidis, E. and Dounias, G., 2003. Application of Fundamental Analysis and Computational Intelligence in Dry Cargo Freight Market. In: Eunite 2003. Third European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems. Proceedings. Verlag-Meinz Publications.

Tsakonas, A. and Dounias, G., 2003. Decision Making in the Medical Domain: Comparing the Effectiveness of GP-Generated Fuzzy Intelligent Structures. In: Eunite 2003. Third European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems. Proceedings. Verlag-Meinz Publications.

Tsakonas, A. and Dounias, G., 2002. Hierarchical Classification Trees Using Type-Constrained Genetic Programming. In: First International IEEE on Intelligent Systems, 2002. Proceedings. Vol.3. IEEE, pp. 3-7.

V

Vasilko, M., 2000. Design synthesis for dynamically reconfigurable logic systems. PhD Thesis (PhD). Bournemouth University.

Vine, D. S. G., 1998. Time-domain concatenative text-to-speech synthesis. PhD Thesis (PhD). Bournemouth University.

Z

Zliobaite, I., Bifet, A., Pfahringer, B. and Holmes, G., 2011. Active learning with evolving streaming data. In: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), 5-9 September 2011, Athens, Greece.

Zliobaite, I., 2011. Combining similarity in time and space for training set formation under concept drift. Intelligent Data Analysis, 15 (4), pp. 589-611.

Zliobaite, I., 2011. Identifying hidden contexts. In: PAKDD2011: The 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 24-27 May 2011, Shenzhen, China. (Unpublished)

Zliobaite, I., 2010. Change with delayed labeling: when is it detectable? In: The 5th International Workshop on Chance Discovery - IWCD5 in IEEE ICDM'10, pp. 843-850.

Zliobaite, I. and Pechenizkiy, M., 2010. Learning with actionable attributes: attention – boundary cases! In: The 2010 ICDM Workshop on Domain Driven Data Mining, pp. 1021-1028.

This list was generated on Wed Apr 23 20:37:02 2014 IST.