Prof Geoff Webb - Researcher Profile

Biography

Geoff works in the Faculty of Information Technology at Monash University as a Professor.

Related Links:

• My personal web page

Research & Supervision Interests

I have published more than 150 scientific papers and am the author of the commercial data mining software package , a system that embodies many of my research contributions in the area of data mining. 

I have been the chief investigator on national competitive grants totalling more than $4.4 million, including $1.75 million as lead investigator.

Keywords

Bioinformatics, Data mining, Knowledge acquisition, Machine learning, User modelling

Qualifications

PhD (Computer Science)

Institution: La Trobe University

Year awarded: 1987

Bachelor of Arts

Institution: La Trobe University

Year awarded: 1982

Publications

Books

Sammut, C., Webb, G. (eds), 2011, Encyclopedia of Machine Learning, Springer, New York NY USA.

Webb, G., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds), 2010, Proceedings of the 10th IEEE International Conference on Data Mining, IEEE Computer Society, Los Alamitos CA USA.

Fan, W., Hsu, W., Webb, G., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (eds), 2010, Proceedings of the 10th IEEE International Conference on Data Mining Workshops, IEEE Computer Society, Los Alamitos CA USA.

Yang, Q., Webb, G. (eds), 2006, PRICAI 2006: Trends in Artificial Intelligence, Springer, Berlin Germany.

Webb, G., Yu, X. (eds), 2004, Proceedings of the 17th Australian Joint Conference on Artificial Intelligence, Springer, Germany.

Book Chapters

Yang, Y., Webb, G., Wu, X., 2010, Discretization methods, in Data Mining and Knowledge Discovery Handbook, eds Oded Maimon and Lior Rokach, Springer Science+Business Media, New York NY USA, pp. 101-116.

Webb, G.I., 2006, Anytime learning and classifications for online applications, in Advances in Intelligent IT, eds Yuefeng Li, Mark Looi and Ning Zhong, IOS Press, Amsterdam The Netherlands, pp. 7-12.

Yang, Y., Webb, G.I., 2006, Discretization for data mining, in Encyclopedia of Data Warehousing and Mining, eds John Wang, IGI Publishing, Hershey USA, pp. 392-396.

Butler, S.M., Webb, G.I., 2006, Mining group differences, in Encyclopedia of Data Warehousing and Mining, eds John Wang, IGI Publishing, Hershey USA, pp. 795-799.

Yang, Y., Webb, G.I., Wu, X., 2005, Discretization Methods, in The Data Mining and Knowledge Discovery Handbook, eds Oded Maimon and Lior Rokach, Springer Science+Business Media, New York NY USA, pp. 113-130.

Webb, G., 2002, Integrating machine learning with knowledge acquisition, in Expert Systems: The Technology of Knowledge Management and Decision Making for the 21st Century, eds Cornelius T Leondes, Academic Press, San Diego CA USA, pp. 937-959.

Journal Articles

Mahmood, K., Webb, G.I., Song, J., Whisstock, J.C., Konagurthu, A.S., 2012, Efficient large-scale protein sequence comparison and gene matching to identify orthologs and co-orthologs, Nucleic Acids Research [P], vol 40, issue 6 (Art. ID: e44), Oxford University Press, UK, pp. 1-11.

Webb, G., Boughton, J., Zheng, F., Ting, K.M., Salem, H., 2012, Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification, Machine Learning [P], vol 86, issue 2, Springer, Dordrecht Netherlands, pp. 233-272.

Song, J., Tan, H., Perry, A.J., Akutsu, T., Webb, G.I., Whisstock, J.C., Pike, R.N., 2012, PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites, PLoS ONE [P], vol 7, issue 11 (Art. No.: e50300), Public Library of Science, USA, pp. 1-23.

Zheng, F., Webb, G., Suraweera, P., Zhu, L., 2012, Subsumption resolution: An efficient and effective technique for semi-naive Bayesian learning, Machine Learning [P], vol 87, issue 1, Springer, Dordrecht Netherlands, pp. 93-125.

Song, J., Tan, H., Wang, M., Webb, G.I., Akutsu, T., 2012, TANGLE: Two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences, PLoS ONE [P], vol 7, issue 2 (Art. ID: e30361), Public Library of Science, USA, pp. 1-16.

Song, J., Tan, H., Boyd, S.E., Shen, H., Mahmood, K., Webb, G., Akutsu, T., Whisstock, J., Pike, R., 2011, Bioinformatic approaches for predicting substrates of proteases, Journal of Bioinformatics and Computational Biology [P], vol 9, issue 1, Imperial College Press, UK, pp. 149-178.

Ng, N., Pierce, M.J., Webb, G., Ratnikov, B., Wijeyewickrema, L.C., Duncan, R., Robertson, A.L., Bottomley, S.P., Boyd, S.E., Pike, R.N., 2011, Discovery of amino acid motifs for thrombin cleavage and validation using a model substrate, Biochemistry [P], vol 50, issue 48, American Chemical Society, USA, pp. 10499-10507.

Ting, K., Wells, J., Tan, S., Teng, S., Webb, G.I., 2011, Feature-subspace aggregating: ensembles for stable and unstable learners, Machine Learning [P], vol 82, issue 3, Springer New York LLC, United States, pp. 375-397.

Webb, G., 2011, Filtered-top-k association discovery, WIREs Data Mining and Knowledge Discovery [P], vol 1, issue 3, John Wiley & Sons, Hoboken NJ USA, pp. 183-192.

Song, J., Tan, H., Shen, H., Mahmood, K., Boyd, S.E., Webb, G., Akutsu, T., Whisstock, J., 2010, Cascleave: towards more accurate prediction of caspase substrate cleavage sites, Bioinformatics [P], vol 26, issue 6, Oxford University Press, UK & USA, pp. 752-760.

Mahmood, K., Konagurthu, A.S., Song, J., Buckle, A.M., Webb, G.I., Whisstock, J.C., 2010, EGM: Encapsulated gene-by-gene matching to identify gene orthologs and homologous segments in genomes, Bioinformatics [P], vol 26, issue 17, Oxford University Press, UK, USA, pp. 2076-2084.

Webb, G.I., 2010, Self-sufficient itemsets: An approach to screening potentially interesting associations between items, ACM Transactions on Knowledge Discovery from Data [P], vol 4, issue 1, Association for Computing Machinery, New York NY USA, pp. 1-20.

Hui, B., Yang, Y., Webb, G., 2009, Anytime classification for a pool of instances, Machine Learning [P], vol 77, issue 1, Springer, Dordrecht GZ Netherlands, pp. 61-102.

Yang, Y., Webb, G., 2009, Discretization for naive-Bayes learning: Managing discretization bias and variance, Machine Learning [P], vol 74, issue 1, Springer, Dordecht GZ Netherlands, pp. 39-74.

Song, J., Tan, H., Mahmood, K., Law, R.H.P., Buckle, A.M., Webb, G., Akutsu, T., Whisstock, J., 2009, Prodepth: Predict residue depth by support vector regression approach from protein sequences only, PLoS ONE [P], vol 4, issue 9 (e7072), Public Library of Science, USA, pp. 1-14.

Novak, P., Lavrac, N., Webb, G., 2009, Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining, Journal Of Machine Learning Research [P], vol 10, Microtome Publishing, Brookline MA USA, pp. 377-403.

Webb, G., 2008, Layered critical values: A powerful direct-adjustment approach to discovering significant patterns, Machine Learning, vol 71, issue 2-3, Springer, Dordrecht Netherlands, pp. 307-323.

Yang, Y., Webb, G., Korb, K.B., Ting, K.M., 2007, Classifying under computational resource constraints: anytime classification using probabilistic estimators, Machine Learning, vol 69, issue 1, Springer, Netherlands, pp. 35-53.

Webb, G., 2007, Discovering significant patterns, Machine Learning, vol 68, issue 1, Springer, Netherlands, pp. 1-33.

Webb, G., 2007, Editorial, Data Mining and Knowledge Discovery, vol 15, issue 1, Springer, Netherlands, pp. 1-2.

Faux, N.G., Huttley, G.A., Mahmood, K., Webb, G., Garcia De La Banda, M.J., Whisstock, J., 2007, RCPdb: An evolutionary classification and codon usage database for repeat-containing proteins, Genome Research, vol 17, issue 7, Cold Spring Harbor Lab Press, Woodbury NY USA, pp. 1118-1127.

Yang, Y., Webb, G., Cerquides, J., Korb, K.B., Boughton, J.R., Ting, K.M., 2007, To select or to weigh: A comparative study of linear combination schemes for superparent-one-dependence estimators, IEEE Transactions on Knowledge and Data Engineering, vol 19, issue 12, IEEE Computer Society, New York NY USA, pp. 1652-1665.

Siu, K.K.W., Butler, S.M., Beveridge, T.E., Gillam, J., Hall, C., Kaye, A.H., Lewis, R.A., Mannan, K., McLoughlin, G., Pearson, S.J., Round, A.R., Schultke, E., Webb, G.I., Wilkinson, S.J., 2005, Identifying markers of pathology in SAXS data of malignant tissues of the brain, Nuclear Instruments and Methods in Physics Reseach Section A, vol 548, Elsevier, Netherland, pp. 140-146.

Webb, G., Zhang, S., 2005, K-Optimal rule discovery, Data Mining and Knowledge Discovery, vol 10, issue 1, Springer Science, New York USA, pp. 39-79.

Webb, G., Boughton, J.R., Wang, Z., 2005, Not so naive Bayes: aggregating one-dependence estimators, Machine Learning, vol 58, issue 1, Springer, Dordrecht The Netherlands, pp. 5-24.

Webb, G., Ting, K.M., 2005, On the Application of ROC analysis to predict classification performance under varying class distributions, Machine Learning, vol 58, issue 1, Springer, Dordrecht The Netherlands, pp. 25-32.

Webb, G.I., Zheng, Z., 2004, Multistrategy ensemble learning: reducing error by combining ensemble learning techniques, IEEE Transactions on Knowledge and Data Engineering, vol 16, issue 8, IEEE Computer Society, Los Alamitos USA, pp. 980-991.

Webb, G., Zhang, C., Zhang, s., 2003, Identifying approximate itemsets of interest in large databases, Applied Intelligence, vol 18, issue 1, Springer, Netherlands, pp. 91-104.

Frayman, Y., Rolfe, B.F., Webb, G.I., 2002, Solving regression problems using competitive ensemble models, Lecture Notes in Artificial Intelligence, vol 2557, Springer-Verlag, Berlin Germany, pp. 511-522.

Brain, D., Webb, G.I., 2002, The Need for Low Bias Algorithms in Classification Learning from Large Data Sets, Lecture Notes in Computer Science, vol 2431, Springer-Verlag, Berlin Germany, pp. 62-73.

Webb, G.I., 2001, Candidate Elimination Criteria for Lazy Bayesian Rules, Lecture Notes in Computer Science, vol 2256, Springer, Heidelberg Berlin, pp. 545-556.

Webb, G.I., Zhang, S., 2001, Further Pruning for Efficient Association Rule Discovery, Lecture Notes in Computer Science, vol 2256, Springer-Verlag, Berlin Heidelberg, pp. 605-618.

Webb, G.I., Pazzani, M.J., Billsus, D., 2001, Machine Learning for User Modeling, User Modeling and User-Adapted Interaction, vol 11, issue 1-2, Kluwer Academic Publishers, Netherlands, pp. 19-29.

Zheng, Z., Webb, G.I., 2000, Lazy Learning of Bayesian Rules, Machine Learning: an international journal, vol 41, issue 1, Springer New York LLC, New York NY USA, pp. 53-84.

Webb, G.I., 2000, MultiBoosting: A Technique for Combining Boosting and Wagging, Machine Learning: an international journal, vol 40, Kluwer Academic Publishers, Netherland, pp. 159-196.

Smith, P.A., Webb, G.I., 2000, The efficacy of a low-level program visualization tool for teaching programming concepts to novice programmers, Journal of Educational Computing Research, vol 22, issue 2, Baywood Publishing Company Inc., Amityville New York NY USA, pp. 187-215.

Conference Proceedings

Martinez, A.M., Webb, G.I., Flores, M.J., Gamez, J.A., 2012, Non-disjoint discretization for aggregating one-dependence estimator classifiers, Hybrid Artificial Intelligent Systems: 7th International Conference, Proceedings, Part II, 28 March 2012 to 30 March 2012, Springer-Verlag, Berlin Germany, pp. 151-162.

Salem, H., Suraweera, P., Webb, G.I., Boughton, J.R., 2012, Techniques for efficient learning without search, Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia Conference, Proceedings, Part I, 29 May 2012 to 1 June 2012, Springer-Verlag, Berlin Germany, pp. 50-61.

Webb, G., 2010, Association Discovery (Keynote), Proceedings of the ACM SIGKDD Workshop on Useful Patterns (UP 2010), 25 July 2010, ACM, New York NY USA, p. 7.

Liu, B., Yang, Y., Webb, G., Boughton, J.R., 2009, A comparative study of bandwidth choice in kernel density estimation for Naive Bayesian classification, Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2009), 27 April 2009 to 30 April 2009, Springer-Verlag, Berlin Germany, pp. 302-313.

Ting, K.M., Wells, J.R., Tan, S.C., Teng, S.W., Webb, G., 2009, FaSS: Ensembles for stable learners, Proceedings of the 8th International Workshop on Multipul Classifier Systems (MCS 2009), 10 June 2009 to 12 June 2009, Springer - Verlag, Berlin Germany, pp. 364-374.

Webb, G., 2007, Finding the real patterns, Lecture Notes in Computer Science, 22 May 2007 to 25 May 2007, Springer-Verlag, Berlin Germany, p. 6.

Zheng, F., Webb, G., 2007, Finding the right family: Parent and child selection for averaged one-dependence estimators, Proceedings of the 18th European Conference on Machine Learning (ECML 2007), 17 September 2007 to 21 September 2007, Springer-Verlag, Berlin Germany, pp. 490-501.

Webb, G., 2006, Discovering significant rules, Proceedings of the twelfth ACM SIGKDD international conference on knowledge discovery and data mining (KDD 2006), 20 August 2006 to 23 August 2006, Association for Computing Machinery (ACM), New York NY USA, pp. 434-443.

Zheng, F., Webb, G.I., 2006, Efficient lazy elimination for averaged one-dependence estimators, Proceedings of the 23rd International Conference on Machine Learning, 25 June 2006 to 29 June 2006, Omnipress, http://shop.omnipress.com/index.asp?PageAction=VIEWPROD&ProdID=33, pp. 1113-1120.

Lu, J., Yang, Y., Webb, G.I., 2006, Incremental discretization for Naive-Bayes classifier, Proceedings of the Second International Conference on Advanced Data Mining and Applications (ADMA 2006), 14 August 2006 to 16 August 2006, Springer-Verlag, Germany, pp. 223-238.

Yang, Y., Webb, G., Cerquides, J., Korb, K.B., Boughton, J.R., Ting, K.M., 2006, To select or to weigh: a comparative study of model selection and model weighing for SPODE ensembles, Proceedings of the 17th European Conference on Machine Learning (ECML 2006), 18 September 2006 to 22 September 2006, Springer-Verlag, Germany, pp. 533-544.

Zheng, F., Webb, G., 2005, A comparative study of Semi-naive Bayes methods in classification learning, Proceedings of the 4th Australasian Data Mining Conference, 05 December 2005 to 06 December 2005, University of Technology Sydney, Sydney NSW Australia, pp. 141-156.

Huang, S., Webb, G., 2005, Discarding insignificant rules during impact rule discovery in large, dense databases, Proceedings of Fifth SIAM International Conference on Data Mining, 21 April 2005 to 23 April 2005, Society for Industrial and Applied Mathematics, Philadelphia USA, pp. 541-545.

Yang, Y., Korb, K.B., Ting, K.M., Webb, G., 2005, Ensemble selection for SuperParent-One-Dependence estimators, Proceedings of the 18th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence (AI 2005), 5 December 2005 to 9 December 2005, Springer-Verlag, Berlin Germany, pp. 102-112.

Huang, S., Webb, G., 2005, Pruning derivative partial rules during impact rule discovery, Proceedings of the 9th Pacific-Asia Conference in Advances in Knowledge Discovery and Data Mining (PAKDD 2005), 18 May 2005 to 20 May 2005, Springer-Verlag, Berlin Germany, pp. 71-80.

Newlands, D.A., Webb, G.I., 2004, Alternative strategies for decision list construction, Proceedings of the Fourth International Conference on Data Mining Including Building Applications for CRM & Competitive Intelligence, 01 December 2003 to 03 December 2003, WIT Press, Southampton UK, pp. 265-273.

Newlands, D.A., Webb, G.I., 2004, Convex hulls as an hypothesis language bias, Proceedings of the Fourth International Conference on Data Mining Including Building Applications for CRM & Competitive Intelligence, 01 December 2003 to 03 December 2003, WIT Press, Southampton UK, pp. 285-294.

Huang, S., Webb, G., 2004, Efficiently identifying exploratory rules' significance, Proceedings of the 3rd Australasian Data Mining Conference, 06 December 2004 to 07 December 2004, University of Technology Sydney, Broadway NSW Australia, pp. 169-182.

Thiruvady, D.R., Webb, G., 2004, Mining negative rules using GRD, Proceedings of the 8th Pacific-Asia Conference in Advances in Knowledge Discovery and Data Mining (PAKDD 2004), 26 May 2004 to 28 May 2004, Springer-Verlag Berlin, Berlin Germany, pp. 161-165.

Wang, Z., Webb, G., Zheng, F., 2004, Selective augmented Bayesian network classifiers based on rough set theory, Proceedings of the 8th Pacific-Asia Conference in Advances in Knowledge Discovery and Data Mining (PAKDD 2004), 26 May 2004 to 28 May 2004, Springer-Verlag Berlin, Berlin Germany, pp. 319-328.

Butler, S.M., Webb, G.I., Lewis, R.A., 2003, A case study in feature invention for breast cancer diagnosis using X-ray scatter images, Proceedings of the 16th Australian Conference on AI: Advances in Artificial Intelligence (AI 2003, 3 December 2003 to 5 December 2003, Springer-Verlag, NY USA, pp. 677-685.

Shi, H., Wang, Z., Webb, G.I., Huang, H., 2003, A new restricted Bayesian network classifier, Proceedings of the 7th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2003), 30 April 2003 to 2 May 2003, Springer-Verlag, Berlin Germany, pp. 265-270.

Wang, Z., Webb, G.I., Zheng, F., 2003, Adjusting dependence relations for semi-lazy TAN classifiers, Proceedings of the 16th Australian Conference on AI: Advances in Artificial Intelligence (AI 2003, 3 December 2003 to 5 December 2003, Springer-Verlag, NY USA, pp. 453-465.

Webb, G.I., Butler, S., Newlands, D., 2003, On detecting differences between groups, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 24 August 2003 to 27 August 2003, The Association for Computing Machinery, New York USA, pp. 256-265.

Yang, Y., Webb, G.I., 2003, On why discretization works for Naive-Bayes classifiers, Proceedings of the 16th Australian Conference on AI: Advances in Artificial Intelligence (AI 2003, 3 December 2003 to 5 December 2003, Springer-Verlag, NY USA, pp. 440-452.

Webb, G.I., 2003, Preliminary investigations into statistically valid exploratory rule discovery, Proceedings of the 2nd Australasian Data Mining Workshop, 08 December 2003 to 12 December 2003, University of Technology, Sydney NSW Australia, pp. 1-9.

Yang, Y., Webb, G.I., 2003, Weighted proportional k-interval discretization for naive-Bayes classifiers, Proceedings of the 7th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2003), 30 April 2003 to 2 May 2003, Springer-Verlag, Berlin Germany, pp. 501-512.

Yang, Y., Webb, G.I., 2002, A comparative study of discretization methods for naive-Bayes classifiers, Proceedings of The 2002 Pacific Rim Knowledge Acquisition Workshop, 18 August 2002 to 19 August 2002, Japanese Society for Artificial Intelligence, Japan, pp. 159-173.

Wang, Z., Webb, G.I., 2002, A heuristic lazy Bayesian rule algorithm, ADM02: Proceedings: Australasian Data Mining Workshop, 03 December 2002 to 03 December 2002, University of Technology Sydney, Sydney NSW Australia, pp. 57-63.

Webb, G.I., Boughton, J., Wang, Z., 2002, Averaged One-Dependence Estimators: Preliminary results, ADM'02: Proceedings: Australasian Data Mining Workshop, 03 December 2002 to 03 December2002, University of Technology, Sydney NSW Australia, pp. 65-73.

Wang, Z., Webb, G.I., 2002, Comparison of lazy Bayesian rule and tree-augmented Bayesian learning, Proceedings: 2002 IEEE International Conference on Data Mining: ICDM 2002, 09 December 2002 to 12 December 2002, IEEE Computer Society, Los Alamitos USA, pp. 490-497.

Pearce, J.E., Webb, G.I., Shaw, R.N., Garner, B., 2002, Experimentation and self learning in continuous database marketing, Proceedings: 2002 IEEE International Conference on Data Mining: ICDM 2002, 09 December 2002 to 12 December 2002, IEEE Computer Society, Los Alamitos USA, pp. 775-778.

Webb, G.I., Brain, D., 2002, Generality is predictive of prediction accuracy, Proceedings of The 2002 Pacific Rim Knowledge Acquisition Workshop, 18 August 2002 to 19 August 2002, Japanese Society for Artificial Intelligence, Japan, pp. 117-130.

Yang, Y., Webb, G.I., 2002, Non-Disjoint Discretization for Naive-Bayes Classifiers, Proceedings of the 19th International Conference on Machine Learning, 08/07/2002 to 12/07/2002, Morgan Kaufmann Publishers Inc., San Francisco CA, pp. 666-673.

Webb, G.I., 2001, Discovering associations with numeric variables, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, ACM Press, New York NY USA, pp. 383-388.

Yang, Y., Webb, G.I., 2001, Proportional k-Interval Discretization for Naive-Bayes Classifiers, Lecture Notes in Computer Science, 3-7/2001, Springer Berlin/Heidelberg, Germany, pp. 564-575.

Webb, G.I., 2000, Efficient search for association rules, The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 20/08/2000-23/08/2000, ACM Press, New York NY USA, pp. 99-107.

Other

Webb, G., Yang, Y., Boughton, J.R., Korb, K.B., Ting, K.M., 2005, Classifying under Computational Resource Constraints: Anytime Classification Using Probabilistic Estimators, Monash University, Melbourne Vic Australia, pp. 1-24.

Webb, G., 2005, K-Optimal Pattern Discovery: An Efficient and Effective Approach to Exploratory Data Mining (Keynote Talk), Proc. of 18th Australian Joint Conference on Artificial Intelligence AI 2005: Advances in Artificial Intelligence, Springer-Verlag, Berlin Germany.

Webb, G., 2003, Association rules, Lawrence Erlbaum Associates Inc, Mahwah New Jersey USA, pp. 25-39.

Teaching Commitment

Professor Geoff Webb is the Chief Examiner for the following units in the Faculty of IT;

• FIT2004 - Algorithms and data structures
• FIT5158 - Customer relationship management and data mining
• FIT5047 - Intelligent systems

Geoff teaches the following units in the Faculty of IT;

• FIT2004 - Algorithms and data structures
• FIT5158 - Customer relationship management and data mining
• FIT5047 - Intelligent systems

 

Grants

Title:

A bioinformatic approach to inter functional interactions within protein sequences.

Investigators:

Webb, G

Funding:

(2007 - 2011). US Air Force Research Laboratory Asian Office of Aerospace Research And Development.

Title:

Development of a collaborative environment for high throughput biology discovery pipelines.

Investigators:

Whisstock, J, Buckle, A, Webb, G, Abramson, D, Bottomley, S, Adler, B, Green, D, Rossjohn, J, Smith, A, Garcia De La Banda, M, Coppel, R

Funding:

(2005 - 2010). Australian Research Council (ARC).

(2005 - 2010). Monash University.

Title:

Development of a collaborative online environment and workbench for the investigation of protein folding.

Investigators:

Buckle, A, Bottomley, S, Webb, G, Fulton, K, Plaxco, K

Funding:

(2005 - 2010). Australian Research Council (ARC).

(2006 - 2010). Monash University.

Title:

Discovering justified knowledge from data.

Investigators:

Webb, G

Funding:

(2007 - 2011). Australian Research Council (ARC).

Title:

Dynamic dimensionality selection for Bayesian classifier ensembles.

Investigators:

Webb, G

Funding:

(2012 - 2016). US Air Force Research Laboratory Asian Office of Aerospace Research And Development.

Title:

Extending association rule discovery to numeric data.

Investigators:

Webb, G

Funding:

(2004 - 2008). Australian Research Council (ARC).

(2004 - 2008). Monash University.

Title:

Horizon Scanning for online workplace safety publications.

Investigators:

Webb, G

Funding:

(2010 - 2014). Transport Accident Commission.

(2010 - 2014). Victorian WorkCover Authority (WorkSafe Victoria).

Title:

Intelligent Collaborative Care Management.

Investigators:

Georgeff, M, Schmidt, H, Thompson, S, Webb, G

Funding:

(2007 - 2011). Australian Research Council (ARC).

(2007 - 2011). British Telecommunications PLC.

(2007 - 2011). Royal Melbourne Institute of Technology (RMIT).

(2008 - 2012). Australian Research Council (ARC).

(2008 - 2012). British Telecommunications PLC.

Title:

Joint Study Agreement.

Investigators:

Webb, G

Funding:

(2005 - 2009). International Business Machines Corporation (IBM) - VIC.

Title:

Knowledge Discovery from Data in the Context of Prior Beliefs.

Investigators:

Webb, G, Nicholson, A

Funding:

(2012 - 2016). Australian Research Council (ARC).

(2012 - 2016). Monash University.

Title:

Learning complex classifiers without search.

Investigators:

Webb, G

Funding:

(2011 - 2014). Australian Research Council (ARC).

(2011 - 2014). Monash University.

(2011 - 2015). Australian Research Council (ARC).

Title:

Resource-bounded adaptive inference of accurate conditional probability estimates from data.

Investigators:

Webb, G, Korb, K, Ting, K

Funding:

(2005 - 2009). Australian Research Council (ARC).

(2005 - 2009). Monash University.

(2007 - 2011). Australian Research Council (ARC).

Title:

Australian High Performance Computational Structural Biology Facility.

Investigators:

Buckle, A, Whisstock, J, Wilce, M, Smith, A, Bottomley, S, Abramson, D, Webb, G, Garcia De La Banda, M, Appelbe, W, Coppel, R

Funding:

(2008 - 2012). Australian Research Council (ARC).

(2008 - 2012). Monash University.

(2008 - 2012). Victorian Partnership For Advanced Computing (VPAC).

Title:

Methods and software for efficiently solving the transportation crewing problem.

Investigators:

Wallace, M, Webb, G, Boland, N, Evans, I, Gu, H

Funding:

(2008 - 2012). Australian Research Council (ARC).

(2008 - 2012). Constraint Technologies International.

(2009 - 2013). Australian Research Council (ARC).

(2009 - 2013). Constraint Technologies International.

(2009 - 2014). University of Newcastle.

Title:

Improved process control and quality in sheet metal forming using machine learning techniques.

Investigators:

Webb, G, Hodgson, P

Funding:

(2001 - 2005). Australian Research Council (ARC).

(2001 - 2005). Ford Motor Company of Australia Ltd.

Title:

Learning efficient accurate Bayesian classifiers from data.

Investigators:

Webb, G

Funding:

(2002 - 2007). Australian Research Council (ARC).

Title:

Mining knowledge from quantitative data.

Investigators:

Webb, G, Zhang, C

Funding:

(2003 - 2009). Australian Research Council (ARC).

Title:

Integrated Intelligent Decision Support for Field Design and Management of Census Operations in Australia.

Investigators:

Churilov, L, Webb, G, Neiger, D

Funding:

(2007 - 2011). Australian Bureau of Statistics (ABS).

(2007 - 2011). Australian Research Council (ARC).