Geoff Webb

Geoffrey I. Webb (also known as Geoff Webb) is Professor of Computer Science at Monash University, Founder and Director of Data Mining software development and consultancy company G. I. Webb and Associates,^[1] and former Editor-in-Chief of the journal Data Mining and Knowledge Discovery.^[2] Before joining Monash University he was on the faculty at Griffith University from 1986 to 1988 and then at Deakin University from 1988 to 2002.

Webb has published more than 190 scientific papers in the fields of machine learning, data science, data mining, data analytics, big data and user modeling.^[3] He is an editor of the Encyclopedia of Machine Learning.^[4]

Webb created the Averaged One-Dependence Estimators machine learning algorithm^[5] and its generalization Averaged N-Dependence Estimators ^[6] and has worked extensively on statistically sound association rule learning.^{[7][8][9] [10]} His early work included advocating the use of machine learning to create black box user models;^[11] and interactive machine learning.^[12][13]

Webb's awards include IEEE Fellow,^[14] the IEEE International Conference on Data Mining Outstanding Service Award,^[15] an Australian Research Council Outstanding Researcher Award^[16] and multiple Australian Research Council Discovery Grants.^[17]

Webb is a Foundation Member of the Editorial Advisory Board of the journal Statistical Analysis and Data Mining, Wiley Inter Science.^[18] He has served on the Editorial Boards of the journals Machine Learning, ACM Transactions on Knowledge Discovery in Data,User Modeling and User Adapted Interaction,and Knowledge and Information Systems.

External links

• Webb's home page
• G. I. Webb and Associates

References

1. "G. I. Webb and Associates"
2. "Data Mining and Knowledge Discovery Journal" Retrieved on 2013-10-20.
3. Geoff Webb's publications indexed by Google Scholar, a service provided by Google
4. "Encyclopedia of Machine Learning"
5. Webb, Geoffrey; J. Boughton; Z. Wang (2005). "Not So Naive Bayes: Aggregating One-Dependence Estimators" (PDF). Machine Learning 58 (1): 5–24. doi:10.1007/s10994-005-4258-6.
6. Webb, Geoffrey; J. Boughton; F. Zheng; K.M. Ting; H. Salem (2012). "Learning by extrapolation from marginal to full-multivariate probability distributions: Decreasingly naive Bayesian classification". Machine Learning 86 (2): 233–272. doi:10.1007/s10994-011-5263-6.
7. Webb, Geoffrey (2007). "Discovering Significant Patterns". Machine Learning 68 (1): 1–33. doi:10.1007/s10994-007-5006-x.
8. Webb, Geoffrey (2008). "Layered Critical Values: A Powerful Direct-Adjustment Approach to Discovering Significant Patterns". Machine Learning 71 (2-3): 307–323. doi:10.1007/s10994-008-5046-x.
9. Webb, Geoffrey (2010). "Self-Sufficient Itemsets: An Approach to Screening Potentially Interesting Associations Between Items". Transactions on Knowledge Discovery from Data 4: 3:1–3:20. doi:10.1145/1644873.1644876.
10. Webb, Geoffrey (2011). "Filtered-top-k Association Discovery". WIREs Data Mining and Knowledge Discovery 1 (3): 183–192. doi:10.1002/widm.28.
11. Webb, Geoffrey; M. Kuzmycz (1996). "Feature based modelling: a methodology for producing coherent, consistent, dynamically changing models of agents' competencies". User Modeling and User-Adapted Interaction 5 (2): 117–150. doi:10.1007/BF01099758.
12. Webb, Geoffrey (1996). "Integrating Machine Learning With Knowledge Acquisition Through Direct Interaction With Domain Experts". Knowledge-Based Systems 9: 253–266. doi:10.1016/0950-7051(96)01033-7.
13. Webb, Geoffrey; J. Wells; Z. Zheng (1999). "An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition". Machine Learning 35 (1): 5–14.
14. "IEEE Fellows 2015"
15. "IEEE Data Mining Awards"
16. Discovery Projects Funding Outcomes for Projects Commencing in 2014
17. "Discovery Projects Funding Outcomes"
18. Statistical Analysis and Data Mining Editorial Board