Irena Koprinska's publications

PUBLICATIONS

DBLP

ACM

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Book Chapters

I. Koprinska and S. Carrato (2003), Segmentation Techniques for Video Sequences in the domain of MPEG compressed data, In Intelligent Integrated Media Communication Techniques, Kluwer Academic Publishers.
M. Kubat, I. Koprinska, and G. Pfurtscheller (1998), Learning to Classify Biological Signals, In Machine Learning, Data Mining and Knowledge Discovery: Methods and Applications, R. Michalski, I. Bratko and M. Kubat (eds.), pp. 409-428, John Wiley and Sons Ltd. [ps.gz]

Journal Papers

D. Perera, J. Kay, I. Koprinska, K. Yacef, O. Zaiane (2009). Clustering and Sequential Data Mining of Online Collaborative Learning Data, IEEE Transactions on Knowledge and Data Engineering, volume 21, issue 6, pp.:759 - 772. [pdf ]
J. Chan, I. Koprinska and J. Poon (2008). Semi-supervised Classification Using Bridging, International Journal of Artificial Intelligence Tools, 17(3), pp.415-431.[pdf ]
I. Koprinska, J. Poon, J. Clark, J. Chan (2007). Learning to Classify E-mail, Information Sciences, 177, pp.2167-2187, Elsevier. [pdf]
D. Cummins, K. Yacef, I. Koprinska (2006). A Sequence Based Recommender System for Learning Resources, Australian Journal of Intelligent Information Processing Systems, 9(2), pp.49-56. [pdf]
I. Koprinska and S. Carrato (2002). Hybrid Rule-Based/Neural Approach for Segmentation of MPEG Compressed Video, Multimedia Tools and Applications, 18, pp. 187-212, Springer. [pdf]
I. Koprinska and S. Carrato (2001). Video Segmentation: A Survey, Signal Processing: Image Communication, 16(5), pp. 477-500, Elsevier Science. [ps.gz]
I. Koprinska, G. Pfurtscheller, and D. Flotzinger (1996), Sleep Classification in Infants by Decision Tree-Based Neural Network, Artificial Intelligence in Medicine, v.8(4), pp. 387-401, Elsevier Science.
I. Ivanova (Koprinska) and M. Kubat (1995), Initialization of Neural Networks by Means of Decision Trees, Knowledge Based Systems, (special issue on Knowledge-Based Neural Networks), 8(6), pp. 333- 344, Elsevier Science.

Refereed Conference Papers

T. O'Keefe and I. Koprinska (2009). Feature Selection and Weighting in Sentiment Analysis, in Proc. Australasian Document Computing Symposium (ADCS), 8 pages, Sydney, Australia, in press. [pdf]
A. Setiawan, I. Koprinska and V. Agelidis (2009). Very Short-Term Electricity Load Demand Forecasting Using Support Vector Regression, in Proc. International Joint Conference on Neural Networks (IJCNN'09), June 14-19, Atlanta, USA, IEEE Press.
I. Koprinska (2009). Comparison of Feature Selection Methods for Classification of Brain-Computer Interface Data, in Proc. Workshop on Advances and Issues in Biomedical Data Mining, Pacific-Asia Knowledge Discovery and Data Mining (PAKDD'09), 27-30 April, Bangkok, Thailand, Proc. of PAKDD AIBDM Workshop. An extended version selected for publication in Advanced Techniques for Data Mining and Knowledge Discovery, PAKDD'2009 Post Proceedings, Lecture Notes in Computer Science 5669, 2009. [pdf]
O. AlZoubi, I. Koprinska and R. Calvo (2008). Classification of Brain-Computer Interface Data, in Proc. Australasian Data Mining Conference (AusDM'08), 27-28 November, Adelaide, Australia, pp.123-132. [pdf]
J. Chan, J. Poon, I. Koprinska (2007). Enhancing the Performance of Semi-supervised Classification Algorithms with Bridging, in Proc. 20th International Florida Artificial Intelligence Research Society Conference (FLAIRS), AAAI Press, pp.580-586. [on-line proceedings]
Perera, D., J. Kay, K. Yacef and I. Koprinska (2007). Mining Learners' Traces From an Online Collaboration Tool, in Proc. Educational Data Mining workshop, held in conjunction with AIED'07. Los Angeles, USA, pp.60-69. [on-line proceedings]
I. Koprinska, D. Deng and F. Feger (2006). Image Classification Using Labelled and Unlabelled Data, in Proc. 14th European Signal and Image Processing Conference (EUSIPCO), Florence, Italy, September 2006. [pdf]
F. Feger and I. Koprinska (2006). Co-training Using RBF Nets and Different Feature Splits, in Proc. IEEE International Joint Conference on Neural Networks (IJCNN), IEEE Press, Vancouver, Canada, July 2006, pp.1878-1885. [pdf]
D. Ler, I. Koprinska, and S. Chawla (2005). Utilising Regression-Based Landmarkers within a Meta-Learning Framework for Algorithm Selection, In Proceedings of the Workshop on Meta-Learning, 22th International Conference on Machine Learning (ICML), 7-11 August Bonn, Germany, pp.44-51. [earlier version as TR]
D. Ler, I. Koprinska, and S. Chawla (2005). A Hill-Climbing Landmarker Generation Algorithm Based on Efficiency and Correlativity Ctiteria, in Proc. of the 18th International Florida Artificial Intelligence Research Society Conference (FLAIRS), AAAI Press, pp.418-423. [TR]
M. Saberi, S. Carrato, I. Koprinska, and J. Clark (2005). Estimation of the Hierarchical Structure of a Video Sequence Using MPEG-7 Descriptors and GCS, in Proc. of the 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES), Lecture Notes in Computer Science 3682, pp.8-15, 14-16 September, Melbourne, Australia.
I. Koprinska and J. Clark, Video Summarization and Browsing Using Growing Cell Structures (2004), in Proc. IEEE International Joint Conference on Neural Networks (IJCNN), vol. 4, pp. 2601-2606, IEEE Press, Budapest, Hungary.
D. Ler, I. Koprinska, and S. Chawla (2004). A Landmarker Selection Algorithm Based on Correlation and Efficiency Criteria, In Proc. 17th Australian Joint Conference on Artificial Intelligence, Lecture Notes in Computer Science 3339, Springer, Cairns, Australia. [TR]
D. Ler, I. Koprinska, and S. Chawla (2004). A New Landmarker Generation Algorithm Based on Correlativity, in Proc. of the ACM/IEEE International Conference on Machine Learning and Applications, Louisville, USA, pp.178-185. [pdf]
D. Ler, I. Koprinska, and S. Chawla (2004). Comparison Between Neuristics Based on Corerlativity and Efficiency for Landmarker Generation, in Proc. of 4th International Conference on Hybrid Intelligent systems, Japan, IEEE Computer Society Press. [TR]
I. Koprinska, J. Clark, and S. Carrato (2004). VideoGCS – A Clustering-Based System for Video Summarization and Browsing, Proc. of the 6th COST 276 Workshop “Information and Knowledge Management for Integrated Media Communication”, Thessaloniki, Greece. [pdf]
E.Crawford, I. Koprinska, and J. Patrick (2004). Phrases and Feature Selection in E-mail Classification, in Proc. of the 9th Australasian Document Computing Symposium (ADCS'04), Melbourne, Australia, pp.59-62. [pdf]
J. Chan, I. Koprinska, J. Poon (2004). Co-training on textual Documents with a Single Natural Feature Set, in Proc. of the 9th Australasian Document Computing Symposium (ADCS’2004), Australia, pp.47-54.
J. Chan, I. Koprinska, J. Poon (2004). Co-training with a Single Natural Feature Set Applied to Email Classification, Proc. of the IEEE International Conference on Web Intelligence (WI'04), pp. 586-589, IEEE press, Beijing, China, [pdf]
I. Koprinska, F. Trieu, J. Poon and J. Clark (2003). E-mail Classification by Decision Forests, in Proc. of the Australasian Document Computing Symposium (ADCS’2003), pp.41-46.
F. Verhein, J. Kay, I. Koprinska and E. McCreath (2003). Classifying Public Announcements for User Communities Proc. of the Australasian Document Computing Symposium (ADCS’2003), pp. 15-24. adcs03.pdf
J. Clark, I. Koprinska, and J. Poon (2003). A Neural Network Based Approach to Automated E-mail Classification, Proc. of the IEEE/WIC Intern. Conference on Web Intelligence (WI'03), IEEE press, Halifax, Canada, October 13-17, pp. 702-705.
H. Mak, I. Koprinska, and J. Poon (2003). Web-Based Movie Recommender Using Text Categorization, Proc. of the IEEE/WIC Intern. Conference on Web Intelligence (WI'03), IEEE press, Halifax, Canada, October 13-17, pp. 602-605. [pdf]
J. Clark, I. Koprinska, and J. Poon (2003). LINGER - A Smart Personal Assistant for E-mail Classification, Proc. of the 13th Intern. Conference on Artificial Neural Networks (ICANN'03), Istanbul, Turkey, June 26-29, pp.274-277
E.Crawford, I. Koprinska, and J. Patrick (2002). A Multi-Learner Approach to E-Mail Classification, Proc. of the Seventh Australasian Document Computing Symposium (ADCS'02), Sydney, Australia. [pdf]
A. Ceguerra and I. Koprinska (2002). Integrating Local and Global Features in Automatic Fingerprint Verification Data, Proc. of the Intern. Conference on Pattern Recognition (ICPR'02), 11-15 August, Quebec City, Canada, vol.3, pp.347-350.
A. Ceguerra and I. Koprinska (2002). Automatic Fingerprint Verification Using Neural Networks, Proc. of the Intern. Conference on Artificial Neural Networks (ICANN'02), Lecture Notes in Computer Science 2415, Jose R. Dorronsoro (ed.), pp.1281-1286, 27-30 August, Madrid, Spain. [pdf]
K. Jackson and I. Koprinska (2002). DNA Microarray Clustering Using Growing Self Organizing Networks, Proc. of the 9th Intern. Conference on Neural Information Processing (ICONIP'02), 18-22 November, Singapore, pp.805-808. [pdf]
I. Koprinska and N. Kasabov (2000). Evolving fuzzy neural network for camera operations recognition, Proc. of the Intern. Conference on Pattern Recognition (ICPR'00), Barcelona, Spain, 3-7 Sept., pp.523-526
D. Deng, I. Koprinska, and N. Kasabov (1999). RICBIS: New Zealand Repository for Intelligent Connectio-nist-Based Information Systems, Proc. of the ICONIP'99 Workshop, Dunedin, NZ, 22-24 Nov., pp.182-187.
M. Kubat and I. Koprinska (1998), Initialization of Neural Network Architectures, Proc. of the 2nd International Conference on Non-Linear Problems in Aviation and Aerospace, Daytona Beach, Florida, April 29-May 1.
I. Koprinska and S. Carrato (1998). Video Segmentation of MPEG Compressed Data, Proc. of the 5th IEEE International Conference on Electronics, Circuits and Systems, ICECS'98, 7-10 September, Lisboa, Portugal, vol.2, pp. 243-246.
I. Koprinska and S. Carrato (1998). Segmentation of Compressed Video by Learning Vector Quantizer, Proc. of the International Conference on Engineering Applications of Neural Networks (EANN'98), 10-12 June 1998, Gibraltar, pp. 9-16. [ps.gz]
I. Koprinska and S. Carrato (1998). Detecting and Classifying Video Shot Boundaries in MPEG Compressed Sequences, Proc. of the European Signal Processing Conference (EUSIPCO'98),special session on Multimedia Signal Processing, 8-11 September 1998, Island of Rhodes, Greece, pp. 1729-1732.
I. Koprinska and S. Carrato (1997). Camera Operation Detection in MPEG Video Data by Means of Neural Networks, Proc. of the COST 254 Workshop on Emerging Technologies for Communication Terminals, pp. 300-304, Toulouse, France.
G. Agre and I. Koprinska (1996), Case-Based Refinement of Knowledge-Based Neural Networks, Proc. of the International Conference on Intelligent Systems: A Semiotic Perspective, October 20-23, Gaithersberg, MD, USA, pp.221-226.
I. Ivanova (Koprinska) and M. Kubat (1995), Decision-Tree Based Neural Network, Proc. of the 8th European Conference on Machine Learning (ECML'95), Heraclion, Crete, Greece, April 25-27, pp.295-298, Lecture Notes in Artificial Intelligence 912, Springer, N. Lavrac and S. Wrobel (eds.).
I. Ivanova (Koprinska), G. Pfurtscheller, and C. Andrew (1995), AI-Based Classification of Single-Trial EEG Data, Proc. of the 17th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Montreal, Canada, September 20-23, pp.703-704.
M. Kubat and I. Ivanova (Koprinska) (1995), Initialization of RBF Networks with Decision Trees, Proc. of the 5th Belgian-Dutch Conference on Machine Learning (BENELEARN'95), Brussels, Belgium, September, pp.61-70.
I. Ivanova (Koprinska), G. Pfurtscheller, D. Flotzinger and M. Kubat (1995), Tree-Based Neural Network Classification of EEG Data, Proc.of the 3rd European Conference on Engineering and Medicine, Florence, Italy, 30 April - 3 May, pp.429, A. Pedotti and P. Rabischong (eds.)
I. Ivanova (Koprinska) (1994), Integrating Decision Trees and Neural Networks, Proc. of the 6th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA'94), Sofia, Bulgaria, September 21-24, pp.311-320, World Scientific Publ., P. Jorrand and V. Sgurev (eds.).
I. Ivanova (Koprinska), M. Kubat, and G. Pfurtscheller (1994), The System TBNN for Learning of 'Difficult' Concepts, Proc. of the 4th Belgian-Dutch Conference on Machine Learning (BENELEARN'94), Rotterdam, The Netherlands, June, pp.230-241, J.C. Bioch and S.H. Nienhuys-Cheng (eds.)

Others

D. Ler, D. Abraham, E. Crawford, and I. Koprinska (2005). Accurate and Efficient Selection of Voting Ensembles, TR 564, School of Information Technologies, University of Sydney, February 2005.