Associate Professor Irena Koprinska

PhD and MSc (Computer Science), MEd (Higher Education)
Sub Dean (Learning and Teaching)
School of

J12 - The School of Information Technologies
The University of Sydney

Telephone +61 2 9351 3764
Fax +61 2 9351 3838

Website School of Information Technologies

Computer Human Adapted Interaction Research Group

Personal Page

Biographical details

Irena Koprinska is Associate Professor in the School of Information Technologies. Her research interests are in data mining, neural networks, recommender systems and medical informatics, in particular classification and prediction tasks. Irena Koprinska has more than 80 publications in these areas, has served on numerous program committees of leading conferences and undertaken extensive reviewing of conference and journal papers, and grant applications. She has taught both undergraduate and postgraduate courses, including large first year courses, and has received the Faculty Award for Outstanding Teaching in 2008 and the SUITS Best Lecturer Award in 2013. As Sub-Dean (Learning and Teaching), Irena Koprinska is responsible for enhancing the quality of teaching in the faculty and promoting good teaching practice.

Teaching and supervision

COMP3308 - Introduction to Artificial Intelligence

COMP3608 - Intro. to Artificial Intelligence (Adv)

INFO1103 - Introduction to Programming

COMP5318 - Knowledge Discovery and Data Mining

ENGG1801 - Engineering Computing

Selected grants

2008

  • Personalisation; Wobcke W, Xu Y, Nayak R, Greenhalgh A, Curran J, Bain M, Mahidadia A, Compton P, Li Y, Spink A, Kay J, Koprinska I, Yacef K; Smart Services Cooperative Research Centre/Research Support.

2007

  • Sequential pattern analysis in learning traces; Koprinska I, Yacef K, Kay J; University of Sydney/Bridging Support.
  • Glass box data-mining to support scalable learner-centred systems; Yacef K, Kay J, Koprinska I; University of Sydney/Bridging Support.
  • Promoting interdisiplinary learning communities using interactive plasma displays; Yacef K, Koprinska I, Calvo R, Vande Moere A; University of Sydney/TIES Small Grant Scheme.

2006

  • Glass box data-mining to support scalable learner-centred systems; Yacef K, Kay J, Koprinska I; University of Sydney/Bridging Support.
  • Talented Students Projects Computer Lab and Electronic Response Lecture Theatre; Koprinska I, Ryan G, Yacef K, Kay J, Kummerfeld R; University of Sydney/Teaching Innovation Grant - Faculty of Science.

2005

  • Bridging the Gap: Smart Support for the Intergenerational Distributed Family; Kummerfeld R, Kay J, Koprinska I, Poon J, Yacef K; Smart Internet Technology Cooperative Research Centre/Cooperative Research Centres.

2003

  • Video Segmentation and Summarization Using Neural Networks; Koprinska I; DVC Research/Research and Development Scheme: Newly Appointed Staff (NAS).

2002

  • Machine Learning for SPA; Greenhalgh A, Koprinska I, Poon J; Smart Internet Technology Cooperative Research Centre/BLO Project.
  • Knowledge acquisition and Machine Learning, Smart Personal Assistant Project 01; Takatsuka M, Yacef K, Tobias J, Davis J, Kay J, Koprinska I, Poon J; Smart Internet Technology Cooperative Research Centre/Cooperative Research Centres.

2001

  • Natural Adaptive User Interface Prototype, NAUI-Project 01; Davis J, Koprinska I, Poon J, Takatsuka M, Yacef K, Kay J, Tobias J; Smart Internet Technology Cooperative Research Centre/BLO Project.

Selected publications

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Book Chapters

  • Kay, J., Koprinska, I., Yacef, K. (2011). Educational Data Mining to Support Group Work in Software Development Projects. In Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, Ryan S J d Baker (Eds.), Handbook of Educational Data Mining, (pp. 173-185). USA: CRC Press.
  • Koprinska, I. (2010). Feature Selection for Brain-Computer Interfaces. In Theeramunkong, Nattee, Adeodato, Chawla, Christen, Lenca, Poon, Williams (Eds.), New Frontiers in Applied Data Mining: PAKDD 2009 International Workshops - Lecture Notes in Artificial Intelligence - LNAI Volume 5669, (pp. 106-117). Berlin Heidelberg: Springer.
  • Koprinska, I., Carrato, S. (2003). Segmentation techniques for video sequences in the domain of MPEG-compressed data. In Tasic, Najim, Ansorge (Eds.), Intelligent Integrated Media Communication Techniques, (pp. 89-116). Netherlands: Springer.

Journals

  • Pizzato, L., Rej, T., Akehurst, J., Koprinska, I., Yacef, K., Kay, J. (2013). Recommending people to people: the nature of reciprocal recommenders with a case study in online dating. User Modeling and User-Adapted Interaction, 23(5), 447-488. [More Information]
  • Pizzato, L., Akehurst, J., Silvestrini, C., Yacef, K., Koprinska, I., Kay, J. (2012). The effect of suspicious profiles on people recommenders. Lecture Notes in Computer Science (LNCS), 7379, 225-236. [More Information]
  • Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaiane, O. (2009). Clustering and Sequential Pattern Mining of Online Collaborative Learning Data. IEEE Transactions On Knowledge And Data Engineering, 21(6), 759-772.
  • Chan, J., Koprinska, I., Poon, J. (2008). Semi-supervised classification using bridging. International Journal on Artificial Intelligence Tools, 17(3), 415-431.
  • Koprinska, I., Poon, J., Clark, J., Chan, J. (2006). Learning to classify e-mail. Information Sciences, Online Article, 1-21. [More Information]
  • Koprinska, I., Carrato, S. (2002). Hybrid Rule-Based/Neural Approach for Segmentation of MPEG Compressed Video. Multimedia Tools and Applications, 18(3), 187-212.
  • Koprinska, I., Carrato, S. (2002). Temporal Video Segmentation: A Survey. Signal Processing: Image Communication, 16(5), 477-500.

Conferences

  • Xu, M., Berkovsky, S., Koprinska, I., Ardon, S., Yacef, K. (2014). Time Dependency in TV Viewer Clustering. PATCH: 4th International Workshop on Personal Access to Cultural Heritage at the 20th converence on User Modeling, Adaptation, and Personalization (UMAP), online: CEUR-WS.
  • O'Keefe, T., Curran, J., Ashwell, P., Koprinska, I. (2013). An Annotated Corpus of Quoted Opinions in News Articles. 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Stroudsburg, PA USA: Association for Computational Linguistics (ACL).
  • Pareti, S., O'Keefe, T., Konstas, I., Curran, J., Koprinska, I. (2013). Automatically Detecting and Attributing Indirect Quotations. 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), Seattle, USA: Association for Computational Linguistics (ACL).
  • Xu, M., Berkovsky, S., Ardon, S., Triukose, S., Mahanti, A., Koprinska, I. (2013). Catch-up TV Recommendations: Show Old Favourites and Find New Ones. 7th ACM Conference on Recommender Systems (RecSys 2013), New York: ACM Digital Library. [More Information]
  • Koprinska, I., Rana, M., Troncoso, A., Martinez-Alvarez, F. (2013). Combining Pattern Sequence Similarity with Neural Networks for Forecasting Electricity Demand Time Series. The 2013 International Joint Conference on Neural Networks (IJCNN), Piscataway, NJ, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Rana, M., Koprinska, I., Khosravi, A. (2013). Feature Selection for Neural Network-Based Interval Forecasting of Electricity Demand Data. 23rd International Conference on Artificial Neural Networks (ICANN 2013), Berlin: Springer-Verlag. [More Information]
  • Rana, M., Koprinska, I., Khosravi, A., Agelidis, V. (2013). Prediction intervals for electricity load forecasting using neural networks. The 2013 International Joint Conference on Neural Networks (IJCNN), Piscataway, NJ, USA: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]
  • Rana, M., Koprinska, I. (2013). Wavelet Neural Networks for Electricity Load Forecasting - Dealing with Border Distortion and Shift Invariance. 23rd International Conference on Artificial Neural Networks (ICANN 2013), Berlin: Springer-Verlag. [More Information]
  • O'Keefe, T., Pareti, S., Curran, J., Koprinska, I., Honnibal, M. (2012). A Sequence Labelling Approach to Quote Attribution. Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (emnlp-conll 2012), Stroudsburg, PA: Association for Computational Linguistics (ACL).
  • Rana, M., Koprinska, I. (2012). Electricity Load Forecasting Using Non-decimated Wavelet Prediction Methods With Two-Stage Feature Selection. 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Piscataway: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]
  • Koprinska, I., Rana, M., Agelidis, V. (2012). Electricity Load Forecasting: A Weekday-Based Approach. 22nd ICANN International Conference on Artificial Neural Networks, Berlin, Germany: Springer. [More Information]
  • Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T. (2012). Explicit and Implicit User Preferences in Online Dating. 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, Heidelberg, Germany: Springer. [More Information]
  • Rana, M., Koprinska, I., Agelidis, V. (2012). Feature Selection for Electricity Load Prediction. The 19th International Conference on Neural Information Processing (ICONIP2012), Heidelberg: Springer-Verlag. [More Information]
  • Kotillova, A., Koprinska, I., Rana, M. (2012). Statistical and Machine Learning Methods for Electricity Demand Prediction. The 19th International Conference on Neural Information Processing (ICONIP2012), Heidelberg: Springer-Verlag. [More Information]
  • Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T. (2011). CCR - A Content-Collaborative Reciprocal Recommender for Online Dating. 22nd International Joint Conference on Artificial Intelligence, Menlo Park, California, USA: AAAI Press.
  • Hawson, L., Koprinska, I., McLean, A., McGreevy, P. (2011). Deciphering the cues from riders' legs. 7th International Equitation Science Conference, the Netherlands: Wageningen Academic Publishers.
  • Pizzato, L., Rej, T., Yacef, K., Koprinska, I., Kay, J. (2011). Finding someone you will like and who won't reject you. 19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011, Berlin, Heidelberg: Springer. [More Information]
  • Koprinska, I. (2011). Mining Assessment and Teaching Evaluation Data of Regular and Advanced Stream Students. 4th International Conference on Educational Data Mining (EDM 2011), Eindhoven, Netherlands: Technische Universiteit Eindhoven.
  • Koprinska, I., Rana, M., Agelidis, V. (2011). Yearly and seasonal models for electricity load forecasting. International Joint Conference on Neutral Networks (IJCNN) 2011, San Jose: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]
  • Sood, R., Koprinska, I., Agelidis, V. (2010). Electricity Load Forecasting Based on Autocorrelation Analysis. 2010 IEEE Congress on Evolutionary Computation (IEEE-CEC) - WCCI 2010, USA: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]
  • Pizzato, L., Chung, T., Rej, T., Koprinska, I., Yacef, K., Kay, J. (2010). Learning User Preferences in Online Dating. European conference on machine learning and principles and practice of knowledge discovery in databases (2012 ECML PKDD), Spain: ECML PKDD.
  • Pizzato, L., Rej, T., Chung, T., Koprinska, I., Yacef, K., Kay, J. (2010). Reciprocal Recommender System for Online Dating. Fourth ACM Conference on Recommender Systems (RecSys10), USA: Association for Computing Machinery (ACM).
  • Pizzato, L., Rej, T., Chung, T., Yacef, K., Koprinska, I., Kay, J. (2010). Reciprocal Recommenders. 8th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2010), USA: Intelligent Techniques for Web Personalization and Recommender Systems.
  • Pizzato, L., Rej, T., Chung, T., Koprinska, I., Kay, J. (2010). RECON: A Reciprocal Recommender for Online Dating. Fourth ACM Conference on Recommender Systems (RecSys10), USA: Association for Computing Machinery (ACM).
  • Koprinska, I., Sood, R., Agelidis, V. (2010). Variable Selection for Five-Minute Ahead Electricity Load Forecasting. 20th International Conference on Pattern Recognition (ICPR 2010), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Koprinska, I. (2009). Comparison of Feature Selection Methods for Classification of Brain-Computer Interface Data. Workshop on Advances and Issues in Biomedical Data Mining AIBDM 2009 - In association with the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Thailand: Printing House of Thammasat University, Rangsit Campus.
  • O'Keefe, T., Koprinska, I. (2009). Feature Selection and Weighting Methods in Sentiment Analysis. 14th Australasian Document Computing Symposium, Sydney, Australia: School of Information Technologies, University of Sydney.
  • Setiawan, A., Koprinska, I., Agelidis, V. (2009). Very Short-Term Electricity Load Demand Forecasting Using Support Vector Regression. International Joint Conference on Neural Networks IJCNN 2009, United States: Documentation LLC.
  • Al-Zoubi, O., Koprinska, I., Calvo, R. (2008). Classification of Brain-Computer Interface Data. The Seventh Australasian Data Mining Conference (AusDM 2008), Sydney NSW, Australia: Australian Computer Society.
  • Chan, J., Poon, J., Koprinska, I. (2007). Enhancing the Performance of Semi-Supervised Classification Algorithms with Bridging. The Twentieth International Florida Artificial Intelligence Research Society Conference, Menlo Park, California: AAAI Press.
  • Perera, D., Kay, J., Yacef, K., Koprinska, I. (2007). Mining learners' traces from an online collaboration tool. Workshop on Educational Data Mining 2007, online: International Working Group on Educational Data Mining.
  • Cummins, D., Yacef, K., Koprinska, I. (2006). A Sequence Based Recommender System for Learning Resources. ADCS 2006 11th Australasian Document Computing Symposium, Queensland: Faculty of Information Technology, Queensland University of Technology.
  • Feger, F., Koprinska, I. (2006). Co-training Using RBF Nets and Different Feature Splits. 2006 IEEE World Congress on Computational Intelligence - A Joint Conference of the Int Joint Conf on Neural Networks (IJCNN), Fuzzy Systems (FUZZ-IEEE), and Evolutionary Computation (CEC), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Koprinska, I., Deng, D., Felix, F. (2006). Image Classification Using Labelled And Unlabelled Data. 14th European Signal Processing Conference (EUSIPCO 2006), Europe: EURASIP European Association for Signal, Speech and Image Processing.
  • Ler, D., Koprinska, I., Chawla, S. (2005). A Hill-climbing Landmarker Generation Algorithm Based on Efficiency and Correlativity Criteria. The Eighteenth International Florida Artificial Intelligence Research Society Conference - FLAIRS 2005, USA: AAAI Press.
  • Ler, D., Koprinska, I., Chawla, S. (2005). Comparisons between Heuristics Based on Correlativity and Efficiency for Landmarker Generation. Fourth International Conference on Hybrid Intelligent Systems - HIS 2004, Los Alamitos, California, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Saberi, M., Carrato, S., Koprinska, I., Clark, J. (2005). Estimation of the Hierachical Structures of a Video Sequence Using MPEG-7 Descriptors and GCS. 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Germany: Springer.
  • Ler, D., Koprinska, I., Chawla, S. (2005). Utilising Regression-based Landmarkers within a Meta-learning Framework for Algorithm Selection. W10 - Workshop on Meta-Learning : associated with the 22nd International Conference on Machine Learning (ICML 2005), Germany: International Machine Learning Society.
  • Ler, D., Koprinska, I., Chawla, S. (2004). A Landmarker Selection Algorithm Based On Correlation And Efficiency Criteria. 17th Australian Joint Conference on Artificial Intelligence, Berlin: Springer.
  • Ler, D., Koprinska, I., Chawla, S. (2004). A New Landmarker Generation Algorithm Based On Correlativity. The 2004 International Conference on Machine Learning and Applications (ICMLA'04), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Chan, J., Koprinska, I., Poon, J. (2004). Co-Training On Textual Documents With A Single Natural Feature Set. The Ninth Australasian Document Computing Symposium (ADCS 2004), Victoria, Australia: Department of Computer Science and Software Engineering, University of Melbourne.
  • Chan, J., Koprinska, I., Poon, J. (2004). Co-Training With A Single Natural Feature Set Applied To Email Classification. 2004 IEEE/WIC ACM International Conference on Web Intelligence, Los Alamitos, CA, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Crawford, E., Koprinska, I., Patrick, J. (2004). Phrases And Feature Selection In E-Mail Classification. The Ninth Australasian Document Computing Symposium (ADCS 2004), Victoria, Australia: Department of Computer Science and Software Engineering, University of Melbourne.
  • Koprinska, I., Clark, J. (2004). Video Summarization And Browsing Using Growing Cell Structures. 2004 IEEE International Joint Conference on Neural Networks. (IEEE) Institute of Electrical and Electronics Engineers.
  • Koprinska, I., Clark, J., Carrato, S. (2004). Videogcs - A Clustering-Based System For Video Summarizatiion And Browsing. 6th COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, Greece: Informatics and Telematics Institute, Centre for Research and Technology.
  • Clark, J., Koprinska, I., Poon, J. (2003). A neural network based approach to automatee e-mail classification. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: (IEEE) Institute of Electrical and Electronics Engineers.
  • Verhein, F., Kay, J., Koprinska, I., McCreath, E. (2003). Classifying public announcements for user communities. Eighth Australasian Document Computing Symposium, Canberra: CSIRO ICT Centre.
  • Koprinska, I., Trieu, F., Poon, J., Clark, J. (2003). E-mail classification by decision forests. Eighth Australasian Document Computing Symposium, Canberra: CSIRO ICT Centre.
  • Mak, H., Koprinska, I., Poon, J. (2003). INTIMATE: A web-based movie recommender using text categorization. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: (IEEE) Institute of Electrical and Electronics Engineers.
  • Clark, J., Koprinska, I., Poon, J. (2003). Linger - a smart personal assistant for e-mail classification. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: (IEEE) Institute of Electrical and Electronics Engineers.
  • Crawford, E., Koprinska, I., Patrick, J. (2002). A Multi-Learner Approach to E-mail Classification. ADCS '02 The Seventh Australasian Document Computing Symposium, Sydney, Australia: School of Information Technologies, University of Sydney.
  • Ceguerra, A., Koprinska, I. (2002). Automatic Fingerprint Verification Using Neural Networks. Artificial Neural Networks ICANN 2002 International Conference, Germany: Springer.
  • Jackson, K., Koprinska, I. (2002). DNA Microarray Data Clustering Using Growing Self Organizing Networks. 9th International Conference on Neural Information Processing (ICONIP''02) 4th Asia Pacific Conference on Simulated Evolution and Learning (SEAL''02) 1st International Conference on Fuzzy Systems and Knowledge Discovery (FSKD''02), Singapore: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
  • Ceguerra, A., Koprinska, I. (2002). Integrating Local and Global Features in Automatic Fingerprint Verification. 16th International Conference on Pattern Recognition, Los Alamitos, California: (IEEE) Institute of Electrical and Electronics Engineers.

2014

  • Xu, M., Berkovsky, S., Koprinska, I., Ardon, S., Yacef, K. (2014). Time Dependency in TV Viewer Clustering. PATCH: 4th International Workshop on Personal Access to Cultural Heritage at the 20th converence on User Modeling, Adaptation, and Personalization (UMAP), online: CEUR-WS.

2013

  • O'Keefe, T., Curran, J., Ashwell, P., Koprinska, I. (2013). An Annotated Corpus of Quoted Opinions in News Articles. 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013), Stroudsburg, PA USA: Association for Computational Linguistics (ACL).
  • Pareti, S., O'Keefe, T., Konstas, I., Curran, J., Koprinska, I. (2013). Automatically Detecting and Attributing Indirect Quotations. 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP 2013), Seattle, USA: Association for Computational Linguistics (ACL).
  • Xu, M., Berkovsky, S., Ardon, S., Triukose, S., Mahanti, A., Koprinska, I. (2013). Catch-up TV Recommendations: Show Old Favourites and Find New Ones. 7th ACM Conference on Recommender Systems (RecSys 2013), New York: ACM Digital Library. [More Information]
  • Koprinska, I., Rana, M., Troncoso, A., Martinez-Alvarez, F. (2013). Combining Pattern Sequence Similarity with Neural Networks for Forecasting Electricity Demand Time Series. The 2013 International Joint Conference on Neural Networks (IJCNN), Piscataway, NJ, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Rana, M., Koprinska, I., Khosravi, A. (2013). Feature Selection for Neural Network-Based Interval Forecasting of Electricity Demand Data. 23rd International Conference on Artificial Neural Networks (ICANN 2013), Berlin: Springer-Verlag. [More Information]
  • Rana, M., Koprinska, I., Khosravi, A., Agelidis, V. (2013). Prediction intervals for electricity load forecasting using neural networks. The 2013 International Joint Conference on Neural Networks (IJCNN), Piscataway, NJ, USA: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]
  • Pizzato, L., Rej, T., Akehurst, J., Koprinska, I., Yacef, K., Kay, J. (2013). Recommending people to people: the nature of reciprocal recommenders with a case study in online dating. User Modeling and User-Adapted Interaction, 23(5), 447-488. [More Information]
  • Rana, M., Koprinska, I. (2013). Wavelet Neural Networks for Electricity Load Forecasting - Dealing with Border Distortion and Shift Invariance. 23rd International Conference on Artificial Neural Networks (ICANN 2013), Berlin: Springer-Verlag. [More Information]

2012

  • O'Keefe, T., Pareti, S., Curran, J., Koprinska, I., Honnibal, M. (2012). A Sequence Labelling Approach to Quote Attribution. Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (emnlp-conll 2012), Stroudsburg, PA: Association for Computational Linguistics (ACL).
  • Rana, M., Koprinska, I. (2012). Electricity Load Forecasting Using Non-decimated Wavelet Prediction Methods With Two-Stage Feature Selection. 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Piscataway: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]
  • Koprinska, I., Rana, M., Agelidis, V. (2012). Electricity Load Forecasting: A Weekday-Based Approach. 22nd ICANN International Conference on Artificial Neural Networks, Berlin, Germany: Springer. [More Information]
  • Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T. (2012). Explicit and Implicit User Preferences in Online Dating. 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011, Heidelberg, Germany: Springer. [More Information]
  • Rana, M., Koprinska, I., Agelidis, V. (2012). Feature Selection for Electricity Load Prediction. The 19th International Conference on Neural Information Processing (ICONIP2012), Heidelberg: Springer-Verlag. [More Information]
  • Kotillova, A., Koprinska, I., Rana, M. (2012). Statistical and Machine Learning Methods for Electricity Demand Prediction. The 19th International Conference on Neural Information Processing (ICONIP2012), Heidelberg: Springer-Verlag. [More Information]
  • Pizzato, L., Akehurst, J., Silvestrini, C., Yacef, K., Koprinska, I., Kay, J. (2012). The effect of suspicious profiles on people recommenders. Lecture Notes in Computer Science (LNCS), 7379, 225-236. [More Information]

2011

  • Akehurst, J., Koprinska, I., Yacef, K., Pizzato, L., Kay, J., Rej, T. (2011). CCR - A Content-Collaborative Reciprocal Recommender for Online Dating. 22nd International Joint Conference on Artificial Intelligence, Menlo Park, California, USA: AAAI Press.
  • Hawson, L., Koprinska, I., McLean, A., McGreevy, P. (2011). Deciphering the cues from riders' legs. 7th International Equitation Science Conference, the Netherlands: Wageningen Academic Publishers.
  • Kay, J., Koprinska, I., Yacef, K. (2011). Educational Data Mining to Support Group Work in Software Development Projects. In Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, Ryan S J d Baker (Eds.), Handbook of Educational Data Mining, (pp. 173-185). USA: CRC Press.
  • Pizzato, L., Rej, T., Yacef, K., Koprinska, I., Kay, J. (2011). Finding someone you will like and who won't reject you. 19th International Conference on User Modeling, Adaptation and Personalization, UMAP 2011, Berlin, Heidelberg: Springer. [More Information]
  • Koprinska, I. (2011). Mining Assessment and Teaching Evaluation Data of Regular and Advanced Stream Students. 4th International Conference on Educational Data Mining (EDM 2011), Eindhoven, Netherlands: Technische Universiteit Eindhoven.
  • Koprinska, I., Rana, M., Agelidis, V. (2011). Yearly and seasonal models for electricity load forecasting. International Joint Conference on Neutral Networks (IJCNN) 2011, San Jose: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]

2010

  • Sood, R., Koprinska, I., Agelidis, V. (2010). Electricity Load Forecasting Based on Autocorrelation Analysis. 2010 IEEE Congress on Evolutionary Computation (IEEE-CEC) - WCCI 2010, USA: (IEEE) Institute of Electrical and Electronics Engineers. [More Information]
  • Koprinska, I. (2010). Feature Selection for Brain-Computer Interfaces. In Theeramunkong, Nattee, Adeodato, Chawla, Christen, Lenca, Poon, Williams (Eds.), New Frontiers in Applied Data Mining: PAKDD 2009 International Workshops - Lecture Notes in Artificial Intelligence - LNAI Volume 5669, (pp. 106-117). Berlin Heidelberg: Springer.
  • Pizzato, L., Chung, T., Rej, T., Koprinska, I., Yacef, K., Kay, J. (2010). Learning User Preferences in Online Dating. European conference on machine learning and principles and practice of knowledge discovery in databases (2012 ECML PKDD), Spain: ECML PKDD.
  • Pizzato, L., Rej, T., Chung, T., Koprinska, I., Yacef, K., Kay, J. (2010). Reciprocal Recommender System for Online Dating. Fourth ACM Conference on Recommender Systems (RecSys10), USA: Association for Computing Machinery (ACM).
  • Pizzato, L., Rej, T., Chung, T., Yacef, K., Koprinska, I., Kay, J. (2010). Reciprocal Recommenders. 8th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems (ITWP 2010), USA: Intelligent Techniques for Web Personalization and Recommender Systems.
  • Pizzato, L., Rej, T., Chung, T., Koprinska, I., Kay, J. (2010). RECON: A Reciprocal Recommender for Online Dating. Fourth ACM Conference on Recommender Systems (RecSys10), USA: Association for Computing Machinery (ACM).
  • Koprinska, I., Sood, R., Agelidis, V. (2010). Variable Selection for Five-Minute Ahead Electricity Load Forecasting. 20th International Conference on Pattern Recognition (ICPR 2010), USA: (IEEE) Institute of Electrical and Electronics Engineers.

2009

  • Perera, D., Kay, J., Koprinska, I., Yacef, K., Zaiane, O. (2009). Clustering and Sequential Pattern Mining of Online Collaborative Learning Data. IEEE Transactions On Knowledge And Data Engineering, 21(6), 759-772.
  • Koprinska, I. (2009). Comparison of Feature Selection Methods for Classification of Brain-Computer Interface Data. Workshop on Advances and Issues in Biomedical Data Mining AIBDM 2009 - In association with the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'09), Thailand: Printing House of Thammasat University, Rangsit Campus.
  • O'Keefe, T., Koprinska, I. (2009). Feature Selection and Weighting Methods in Sentiment Analysis. 14th Australasian Document Computing Symposium, Sydney, Australia: School of Information Technologies, University of Sydney.
  • Setiawan, A., Koprinska, I., Agelidis, V. (2009). Very Short-Term Electricity Load Demand Forecasting Using Support Vector Regression. International Joint Conference on Neural Networks IJCNN 2009, United States: Documentation LLC.

2008

  • Al-Zoubi, O., Koprinska, I., Calvo, R. (2008). Classification of Brain-Computer Interface Data. The Seventh Australasian Data Mining Conference (AusDM 2008), Sydney NSW, Australia: Australian Computer Society.
  • Chan, J., Koprinska, I., Poon, J. (2008). Semi-supervised classification using bridging. International Journal on Artificial Intelligence Tools, 17(3), 415-431.

2007

  • Chan, J., Poon, J., Koprinska, I. (2007). Enhancing the Performance of Semi-Supervised Classification Algorithms with Bridging. The Twentieth International Florida Artificial Intelligence Research Society Conference, Menlo Park, California: AAAI Press.
  • Perera, D., Kay, J., Yacef, K., Koprinska, I. (2007). Mining learners' traces from an online collaboration tool. Workshop on Educational Data Mining 2007, online: International Working Group on Educational Data Mining.

2006

  • Cummins, D., Yacef, K., Koprinska, I. (2006). A Sequence Based Recommender System for Learning Resources. ADCS 2006 11th Australasian Document Computing Symposium, Queensland: Faculty of Information Technology, Queensland University of Technology.
  • Feger, F., Koprinska, I. (2006). Co-training Using RBF Nets and Different Feature Splits. 2006 IEEE World Congress on Computational Intelligence - A Joint Conference of the Int Joint Conf on Neural Networks (IJCNN), Fuzzy Systems (FUZZ-IEEE), and Evolutionary Computation (CEC), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Koprinska, I., Deng, D., Felix, F. (2006). Image Classification Using Labelled And Unlabelled Data. 14th European Signal Processing Conference (EUSIPCO 2006), Europe: EURASIP European Association for Signal, Speech and Image Processing.
  • Koprinska, I., Poon, J., Clark, J., Chan, J. (2006). Learning to classify e-mail. Information Sciences, Online Article, 1-21. [More Information]

2005

  • Ler, D., Koprinska, I., Chawla, S. (2005). A Hill-climbing Landmarker Generation Algorithm Based on Efficiency and Correlativity Criteria. The Eighteenth International Florida Artificial Intelligence Research Society Conference - FLAIRS 2005, USA: AAAI Press.
  • Ler, D., Koprinska, I., Chawla, S. (2005). Comparisons between Heuristics Based on Correlativity and Efficiency for Landmarker Generation. Fourth International Conference on Hybrid Intelligent Systems - HIS 2004, Los Alamitos, California, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Saberi, M., Carrato, S., Koprinska, I., Clark, J. (2005). Estimation of the Hierachical Structures of a Video Sequence Using MPEG-7 Descriptors and GCS. 9th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, Germany: Springer.
  • Ler, D., Koprinska, I., Chawla, S. (2005). Utilising Regression-based Landmarkers within a Meta-learning Framework for Algorithm Selection. W10 - Workshop on Meta-Learning : associated with the 22nd International Conference on Machine Learning (ICML 2005), Germany: International Machine Learning Society.

2004

  • Ler, D., Koprinska, I., Chawla, S. (2004). A Landmarker Selection Algorithm Based On Correlation And Efficiency Criteria. 17th Australian Joint Conference on Artificial Intelligence, Berlin: Springer.
  • Ler, D., Koprinska, I., Chawla, S. (2004). A New Landmarker Generation Algorithm Based On Correlativity. The 2004 International Conference on Machine Learning and Applications (ICMLA'04), USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Chan, J., Koprinska, I., Poon, J. (2004). Co-Training On Textual Documents With A Single Natural Feature Set. The Ninth Australasian Document Computing Symposium (ADCS 2004), Victoria, Australia: Department of Computer Science and Software Engineering, University of Melbourne.
  • Chan, J., Koprinska, I., Poon, J. (2004). Co-Training With A Single Natural Feature Set Applied To Email Classification. 2004 IEEE/WIC ACM International Conference on Web Intelligence, Los Alamitos, CA, USA: (IEEE) Institute of Electrical and Electronics Engineers.
  • Crawford, E., Koprinska, I., Patrick, J. (2004). Phrases And Feature Selection In E-Mail Classification. The Ninth Australasian Document Computing Symposium (ADCS 2004), Victoria, Australia: Department of Computer Science and Software Engineering, University of Melbourne.
  • Koprinska, I., Clark, J. (2004). Video Summarization And Browsing Using Growing Cell Structures. 2004 IEEE International Joint Conference on Neural Networks. (IEEE) Institute of Electrical and Electronics Engineers.
  • Koprinska, I., Clark, J., Carrato, S. (2004). Videogcs - A Clustering-Based System For Video Summarizatiion And Browsing. 6th COST 276 Workshop on Information and Knowledge Management for Integrated Media Communication, Greece: Informatics and Telematics Institute, Centre for Research and Technology.

2003

  • Clark, J., Koprinska, I., Poon, J. (2003). A neural network based approach to automatee e-mail classification. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: (IEEE) Institute of Electrical and Electronics Engineers.
  • Verhein, F., Kay, J., Koprinska, I., McCreath, E. (2003). Classifying public announcements for user communities. Eighth Australasian Document Computing Symposium, Canberra: CSIRO ICT Centre.
  • Koprinska, I., Trieu, F., Poon, J., Clark, J. (2003). E-mail classification by decision forests. Eighth Australasian Document Computing Symposium, Canberra: CSIRO ICT Centre.
  • Mak, H., Koprinska, I., Poon, J. (2003). INTIMATE: A web-based movie recommender using text categorization. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: (IEEE) Institute of Electrical and Electronics Engineers.
  • Clark, J., Koprinska, I., Poon, J. (2003). Linger - a smart personal assistant for e-mail classification. 2003 IEEE/WIC Intenational Conference on Web Intelligence, United States: (IEEE) Institute of Electrical and Electronics Engineers.
  • Koprinska, I., Carrato, S. (2003). Segmentation techniques for video sequences in the domain of MPEG-compressed data. In Tasic, Najim, Ansorge (Eds.), Intelligent Integrated Media Communication Techniques, (pp. 89-116). Netherlands: Springer.

2002

  • Crawford, E., Koprinska, I., Patrick, J. (2002). A Multi-Learner Approach to E-mail Classification. ADCS '02 The Seventh Australasian Document Computing Symposium, Sydney, Australia: School of Information Technologies, University of Sydney.
  • Ceguerra, A., Koprinska, I. (2002). Automatic Fingerprint Verification Using Neural Networks. Artificial Neural Networks ICANN 2002 International Conference, Germany: Springer.
  • Jackson, K., Koprinska, I. (2002). DNA Microarray Data Clustering Using Growing Self Organizing Networks. 9th International Conference on Neural Information Processing (ICONIP''02) 4th Asia Pacific Conference on Simulated Evolution and Learning (SEAL''02) 1st International Conference on Fuzzy Systems and Knowledge Discovery (FSKD''02), Singapore: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore.
  • Koprinska, I., Carrato, S. (2002). Hybrid Rule-Based/Neural Approach for Segmentation of MPEG Compressed Video. Multimedia Tools and Applications, 18(3), 187-212.
  • Ceguerra, A., Koprinska, I. (2002). Integrating Local and Global Features in Automatic Fingerprint Verification. 16th International Conference on Pattern Recognition, Los Alamitos, California: (IEEE) Institute of Electrical and Electronics Engineers.
  • Koprinska, I., Carrato, S. (2002). Temporal Video Segmentation: A Survey. Signal Processing: Image Communication, 16(5), 477-500.

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