158.Webb, G.I. (2010). Self-Sufficient Itemsets: An Approach to Screening Potentially Interesting Associations Between Items. Transactions on Knowledge Discovery from Data 4. ACM, pages 3:1-3:20. [Abstract] [Pre-Publication PDF][Link to paper via ACM Digital Library]
157.Song, J., H. Tan, K. Mahmood, R.H.P. Law, A.M. Buckle, G.I. Webb, T. Akutsu, and J.C. Whisstock (2009). Prodepth: Predict Residue Depth by Support Vector Regression Approach from Protein Sequences Only. PLoS ONE 4(9). PLOS, pages e7072. [Abstract] [Link to paper]
156.Hui, B., Y. Yang, and G.I. Webb (2009). Anytime Classification for a Pool of Instances. Machine Learning 77(1). Netherlands: Springer, pages 61-102. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
155.Yang, Y. and G.I. Webb (2009). Discretization for Naive-Bayes Learning: Managing Discretization Bias and Variance. Machine Learning 74(1). Netherlands: Springer, pages 39-74. [Abstract] [Pre-Publication PDF][Link to paper via Springerlink]
154.Novak, P., N. Lavrac, and G.I. Webb (2009). Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining. Journal of Machine Learning Research 10., pages 377-403. [Abstract] [Link to paper on JMLR site ]
153.Liu, B., Y. Yang, G.I. Webb, and J. Boughton (2009). A Comparative Study of Bandwidth Choice in Kernel Density Estimation for Naive Bayesian Classification. In Proceedings of the 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand. Berlin/Heidelberg: Springer.
152.Ting, K.M., J.R. Wells, S.C. Tan, S.W. Teng, and G.I. Webb (2009). {FaSS}: Ensembles for Stable Learners. In Proceedings of the 8th International Workshop on Multiple Classifier Systems, MCS 2009 Reykjavik, Iceland. Berlin: Springer, pages 364-374.
151.Webb, G.I. (2008). Layered Critical Values: A Powerful Direct-Adjustment Approach to Discovering Significant Patterns. Machine Learning 71(2-3). Netherlands: Springer, pages 307-323 [Technical Note]. [Abstract] [Pre-Publication PDF][Link to paper via Springerlink]
150.Webb, G.I. (2008). Multi-Strategy Ensemble Learning, Ensembles of Bayesian Classifiers, and the Problem of False Discoveries. In Proceedings of the Seventh Australasian Data Mining Conference (AusDM 2008) Adelaide, Australia. Australian Computer Society, pages 15.
149.Yang, Y., G.I. Webb, K. Korb, and K-M. Ting (2007). Classifying under Computational Resource Constraints: Anytime Classification Using Probabilistic Estimators. Machine Learning 69(1). Netherlands: Springer, pages 35-53. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
148.Webb, G.I. (2007). Discovering Significant Patterns. Machine Learning 68(1). Netherlands: Springer, pages 1-33. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
147.Webb, G. I. (2007). Tenth Anniversary Edition Editorial. Data Mining and Knowledge Discovery 15(1). Netherlands: Springer, pages 1-2.[Link to editorial via Springerlink]
146.Faux, N.G., G.A. Huttley, K. Mahmood, G.I. Webb, M. Garcia de la Banda, and J.C. Whisstock (2007). RCPdb: An evolutionary classification and codon usage database for repeat-containing proteins. Genome Research 17(1). Woodbury, New York: Cold Spring Harbor Laboratory Press, ISSN 1088-9051/07, pages 1118-1127. [Abstract] [Link to paper via Genome Research On-line]
145.Yang, Y., G.I. Webb, J. Cerquides, K. Korb, J. Boughton, and K-M. Ting (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 (TKDE) 19(12). Los Alamitos, CA: IEEE Computer Society, pages 1652-1665. [Abstract] [Pre-publication PDF][ Link to paper via IEEE]
144.Zheng, F. and G.I. Webb (2007). Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators. In Lecture Notes in Artificial Intelligence 4710: Proceedings of the 18th European Conference on Machine Learning (ECML'07) Warsaw, Poland. Berlin/Heidelberg: Springer-Verlag, pages 490-501. [Abstract] [Pre-publication PDF]
143.Webb, G.I. (2007). Finding the Real Patterns (Extended Abstract). In Zhi-Hua Zhou, Hang Li, Qiang Yang (Ed.), Lecture Notes in Computer Science Vol. 4426 : Advances in Knowledge Discovery and Data Mining Proceedings of the 11th Pacific-Asia Conference, PAKDD 2007 Nanjing, China. Berlin/Heidelberg: Springer, pages 6 ISBN 978-3-540-71700-3.
142.Yang, Y. and G. I. Webb (2006). Discretization for Data Mining. In John Wang (Ed.), The Encyclopedia of Data Warehousing and Mining. Hershey, PA: Idea Group Inc., pages 392-396.[Link to Publisher]
141.Butler, S. and G. I. Webb (2006). Mining Group Differences. In John Wang (Ed.), The Encyclopedia of Data Warehousing and Mining. Hershey, PA: Idea Group Inc., pages 795-799.[Link to Publisher]
140.Zheng, F. and G.I. Webb (2006). Efficient Lazy Elimination for Averaged One-Dependence Estimators. In W. Cohen and A. Moore (Eds.), ACM International Conference Proceeding Series, Vol. 148: The Proceedings of the Twenty-third International Conference on Machine Learning (ICML'06) Pittsburgh, Pennsylvania. New York, NY: ACM Press, pages 1113 - 1120. [Abstract] [Pre-publication PDF][Link to paper via ACM Portal]
139.Webb, G.I. (2006). Discovering Significant Rules. In L. Ungar, M. Craven, D. Gunopulos and T. Eliassi-Rad (Eds.), Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006) Philadelphia, PA. New York: The Association for Computing Machinery, pages 434 - 443. [Abstract] [Pre-publication PDF][Download from ACM Portal]
138.Yang, Y., G.I. Webb, J. Cerquides, K. Korb, J. Boughton, and K-M. Ting (2006). To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles. In J. Furkranz, T. Scheffer and M. Spiliopoulou (Eds.), Lecture Notes in Computer Science 4212: Proceedings of the 17th European Conference on Machine Learning (ECML'06) Berlin, Germany. Berlin/Heidelberg: Springer-Verlag, pages 533-544. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
137.Lu, J., Y. Yang, and G.I. Webb (2006). Incremental Discretization for Naive-Bayes Classifier. In Xue Li, Osmar R. ZaŹane and Zhanhuai Li (Eds.), Lecture Notes in Computer Science 4093: Proceedings of the Second International Conference on Advanced Data Mining and Applications (ADMA 2006) XiĆan, China. Berlin: Springer, pages 223-238. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
136.Webb, G.I. (2006). Anytime Learning and Classification for Online Applications. In Y. Li, M. Looi and N. Zhong (Eds.), Advances in Intelligent IT: Proceedings of the Fourth International Conference on Active Media Technology (AMT'06). [Extended Abstract] Brisbane, Australia. Amsterdam: IOS Press, pages 7-12. [Abstract] [Pre-publication PDF]
135.Webb, G.I. and D. Brain (2006). Generality is Predictive of Prediction Accuracy. In LNAI State-of-the-Art Survey series, 'Data Mining: Theory, Methodology, Techniques, and Applications'. Berlin/Heidelberg: Springer, pages 1-13, (Note: an earlier version of this paper was published in the Proceedings of PKAW 2002, pp 117-130). [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
134.Huang, S. and G.I. Webb (2006). Efficiently Identifying Exploratory Rules' Significance. In LNAI State-of-the-Art Survey series, 'Data Mining: Theory, Methodology, Techniques, and Applications'. Berlin/Heidelberg: Springer, pages 64-77, (Note: an earlier version of this paper was published in S.J. Simoff and G.J. Williams (Eds.), Proceedings of the Third Australasian Data Mining Conference (AusDM04) Cairns, Australia. Sydney: University of Technology, pages 169-182.). [Abstract] [Link to paper via Springerlink]
133.Q. Yang and G. I. Webb (Eds.) (2006). Lecture Notes in Artificial Intelligence 4099: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2006), Guilin, China. Berlin: Springer.
132.Webb, G. I., J. Boughton, and Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning 58(1). Netherlands: Springer, pages 5-24. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
131.Webb, G. I. and K.M. Ting (2005). On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Machine Learning 58(1). Netherlands: Springer, pages 25-32. [Abstract] [Prepublication PDF][Link to paper via Springerlink]
130.Webb, G. I. and S. Zhang (2005). k-Optimal-Rule-Discovery. Data Mining and Knowledge Discovery 10(1). Netherlands: Springer, pages 39-79. [Abstract] [Prepublication PDF][Link to paper via Springerlink]
129.Siu, K.K.W., S.M. Butler, T. Beveridge, J.E. Gillam, C.J. Hall, A.H. Kaye, R.A. Lewis, K. Mannan, G. McLoughlin, S. Pearson, A.R. Round, E. Schultke, G.I. Webb, and S.J. Wilkinson (2005). Identifying markers of pathology in SAXS data of malignant tissues of the brain. Nuclear Instruments and Methods in Physics Research A 548. Elsevier, pages 140-146. [Abstract] [Pre-publication PDF][Link to paper via Science Direct]
128.Yang, Y., G. I. Webb, and X. Wu (2005). Chapter 6: Discretization Methods. In O. Maimon and L. Rokach (Eds.), The Data Mining and Knowledge Discovery Handbook. Berlin: Springer, pages 113-130.
127.Huang, S. and G.I. Webb (2005). Discarding Insignificant Rules During Impact Rule Discovery in Large, Dense Databases. In H. Kargupta, C. Kamath, J. Srivastava and A. Goodman (Eds.), Proceedings of the Fifth SIAM International Conference on Data Mining (SDM'05) [short paper] Newport Beach, CA. Philadelphia, PA: Society for Industrial and Applied Mathematics, pages 541-545. [Abstract] [Pre-publication PDF][Link to SIAM]
126.Huang, S. and G.I. Webb (2005). Pruning Derivative Partial Rules During Impact Rule Discovery. In T.B. Ho, D. Cheung and H. Liu (Eds.), Lecture Notes in Computer Science Vol. 3518: Proceedings of the 9th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2005) Hanoi, Vietnam. Berlin/Heidelberg: Springer, pages 71-80. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
125.Yang, Y., K. Korb, K-M. Ting, and G.I. Webb (2005). Ensemble Selection for SuperParent-One-Dependence Estimators. In S. Zhang and R. Jarvis (Eds.), Lecture Notes in Computer Science 3809: Advances in Artificial Intelligence, Proceedings of the 18th Australian Joint Conference on Artificial Intelligence (AI 2005) Sydney, Australia. Berlin/Heidelberg: Springer, pages 102-111. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
124.Webb, G.I. (2005). K-Optimal Pattern Discovery: An Efficient and Effective Approach to Exploratory Data Mining. In S. Zhang and R. Jarvis (Eds.), Lecture Notes in Computer Science 3809: Advances in Artificial Intelligence, Proceedings of the 18th Australian Joint Conference on Artificial Intelligence (AI 2005)[Extended Abstract] Sydney, Australia. Berlin/Heidelberg: Springer, pages 1-2.[Pre-publication PDF][Link to paper via Springerlink]
123.Zheng, F. and G.I. Webb (2005). A Comparative Study of Semi-naive Bayes Methods in Classification Learning. In S.J. Simoff, G.J. Williams, J. Galloway and I. Kolyshkina (Eds.), Proceedings of the Fourth Australasian Data Mining Conference (AusDM05) Sydney, Australia. Sydney: University of Technology, pages 141-156. [Abstract] [Pre-publication PDF]
122.Webb, G.I. and Z. Zheng (2004). Multistrategy Ensemble Learning: Reducing Error by Combining Ensemble Learning Techniques. IEEE Transactions on Knowledge and Data Engineering 16(8). Los Alamitos, CA: IEEE Computer Society, pages 980-991. [Abstract] [PDF][ Link to paper via IEEE]
121.Thiruvady, D. R. and G. I. Webb (2004). Mining Negative Rules using GRD. In H. Dai, R. Srikant and C. Zhang (Eds.), Lecture Notes in Computer Science Vol. 3056: Proceedings of the Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 04) [Short Paper] Sydney, Australia. Berlin/Heidelberg: Springer, pages 161-165. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
120.Wang, Z., G.I. Webb, and F. Zheng (2004). Selective Augmented Bayesian Network Classifiers Based on Rough Set Theory. In H. Dai, R. Srikant and C. Zhang (Eds.), Lecture Notes in Computer Science Vol. 3056: Proceedings of the Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 04) Sydney, Australia. Berlin/Heidelberg: Springer, pages 319-328. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
119.Newlands, D.A. and G.I. Webb (2004). Convex Hulls as an Hypothesis Language Bias. In N.F.F.E. Ebecken, C.A. Brebbia and A. Zanasi (Eds.), Proceedings of the Fourth International Conference on Data Mining (DATA MINING IV) Rio de Janeiro, Brazil. Southampton, UK: WIT Press, pages 285-294. [Abstract] [Pre-publication PDF][Link to WIT Press]
118.Newlands, D.A. and G.I. Webb (2004). Alternative Strategies for Decision List Construction. In N.F.F.E. Ebecken, C.A. Brebbia and A. Zanasi (Eds.), Proceedings of the Fourth International Conference on Data Mining (DATA MINING IV) Rio de Janeiro, Brazil. Southampton, UK: WIT Press, pages 265-273. [Abstract] [Pre-publication PDF][Link to WIT Press]
117.Huang, S. and G.I. Webb (2004). Efficiently Identifying Exploratory Rules' Significance. In S.J. Simoff and G.J. Williams (Eds.), Proceedings of the Third Australasian Data Mining Conference (AusDM04) Cairns, Australia. Sydney: University of Technology, pages 169-182. [Abstract] [Pre-publication PDF]
116.G. I. Webb and X. Yu (Eds.) (2004). Lecture Notes in Computer Science 3339: Proceedings of the 17th Australian Joint Conference on Artificial Intelligence (AI 2004), Cairns, Australia. Berlin: Springer.
115.Zhang, C., S. Zhang, and G. I. Webb (2003). Identifying Approximate Item-Sets Of Interest In Large Databases. Applied Intelligence 18. Netherlands: Springer, pages 91-104. [Abstract] [Link to paper via Springerlink]
114.Webb, G. I. (2003). Association Rules. In Dr. Nong Ye (Ed.), The Handbook of Data Mining, Chapter 2. Lawrence Erlbaum Associates, pages 25 - 39.[Link to publisher]
113.Webb, G. I., S. Butler, and D. Newlands (2003). On Detecting Differences Between Groups. In P. Domingos, C. Faloutsos, T. Senator, H. Kargupta and L. Getoor (Eds.), Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003) Washington, DC. New York: The Association for Computing Machinery, pages 256-265. [Abstract] [PDF][Paper via ACM Portal]
112.Yang, Y. and G.I. Webb (2003). Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers. In K-Y. Whang, J. Jeon, K. Shim and J. Srivastava (Eds.), Lecture Notes in Artificial Intelligence Vol. 2637: Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'03) Seoul, Korea. Berlin/Heidelberg: Springer-Verlag, pages 501-512. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
111.Shi, H., Z. Wang, G.I. Webb, and H. Huang (2003). A New Restricted Bayesian Network Classifier. In K-Y. Whang, J. Jeon, K. Shim and J. Srivastava (Eds.), Lecture Notes in Artificial Intelligence Vol. 2637: Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'03) Seoul, Korea. Berlin/Heidelberg: Springer-Verlag, pages 265-270. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
110.Rolfe, B., Y. Frayman, G.I. Webb, and P. Hodgson (2003). Analysis of Stamping Production Data with View Towards Quality Management. In Proceedings of the 9th International Conference on Manufacturing Excellence (ICME 03) Melbourne, Australia..[Pre-publication PDF]
109.Rolfe, B, P Hodgson, and G. I. Webb (2003). Improving the Prediction of the Roll Separating Force in a Hot Steel Finishing Mill. In J.A. Meech (Ed.), Intelligence in a Small World - Nanomaterials for the 21st Century. Selected Papers from IPMM-2003 Sendai, Japan. Boca Raton, Florida: CRC-Press.[Pre-publication PDF]
108.Wang, Z., G.I. Webb, and F. Zheng (2003). Adjusting Dependence Relations for Semi-Lazy TAN Classifiers. In T.D. Gedeon and L.C.C. Fung (Eds.), Lecture Notes in Artificial Intelligence Vol. 2903: Proceedings of the 16th Australian Conference on Artificial Intelligence (AI 03) Perth, Australia. Berlin/Heidelberg: Springer, pages 453-465. [Abstract] [Pre-publication PDF ][Link to paper via Springerlink]
107.Butler, S. M., G.I. Webb, and R.A. Lewis (2003). A Case Study in Feature Invention for Breast Cancer Diagnosis Using X-Ray Scatter Images. In T.D. Gedeon and L.C.C. Fung (Eds.), Lecture Notes in Artificial Intelligence Vol. 2903: Proceedings of the 16th Australian Conference on Artificial Intelligence (AI 03) Perth, Australia. Berlin/Heidelberg: Springer, pages 677-685. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
106.Yang, Y. and G. I. Webb (2003). On Why Discretization Works for Naive-Bayes Classifiers. In T.D. Gedeon and L.C.C. Fung (Eds.), Lecture Notes in Artificial Intelligence Vol. 2903: Proceedings of the 16th Australian Conference on Artificial Intelligence (AI 03) Perth, Australia. Berlin/Heidelberg: Springer, pages 440-452. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
105.Webb, G.I. (2003). Preliminary Investigations into Statistically Valid Exploratory Rule Discovery. In S.J. Simoff, G.J. Williams and M. Hegland (Eds.), Proceedings of the Second Australasian Data Mining Conference (AusDM03) Canberra, Australia. Sydney: University of Technology, pages 1-9. [Abstract] [Pre-publication PDF]
104.Webb, G. I. (2002). Integrating Machine Learning with Knowledge Acquisition. In C. T. Leondes (Ed.), Expert Systems, volume 3. San Diego, CA: Academic Press, pages 937-959.[Pre-publication PDF][Link to Publisher]
103.Yang, Y. and G. I. Webb (2002). Non-Disjoint Discretization for Naive-Bayes Classifiers. In C. Sammut and A.G. Hoffmann (Eds.), Proceedings of the Nineteenth International Conference on Machine Learning (ICML '02) Sydney, Australia. San Francisco: Morgan Kaufmann, pages 666-673. [Abstract] [Pre-publication PDF]
102.Pearce, J., G. I. Webb, R. Shaw, and B. Garner (2002). A Framework for Experimentation and Self Learning in Continuous Database Marketing. In Proceedings of the IEEE International Conference on Data Mining (ICDM-2002) Maebashi City, Japan. Los Alamitos, CA: IEEE Computer Society, pages 490-497. [Abstract] [PDF][Link to paper via IEEE xplore]
101.Wang, Z. and G.I. Webb (2002). Comparison of Lazy Bayesian Rule Learning and Tree-Augmented Bayesian Learning. In Proceedings of the IEEE International Conference on Data Mining (ICDM-2002) Maebashi City, Japan. Los Alamitos, CA: IEEE Computer Society, pages 775-778. [Abstract] [PDF][Link to paper via IEEE xplore]
100.Brain, D. and G.I. Webb (2002). The Need for Low Bias Algorithms in Classification Learning From Large Data Sets. In Lecture Notes in Computer Science 2431: Principles of Data Mining and Knowledge Discovery: Proceedings of the Sixth European Conference (PKDD 2002) Helsinki, Finland. Berlin/Heidelberg: Springer-Verlag, pages 62-73. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
99.Frayman, Y., B. Rolfe, P. Hodgson, and G. I. Webb (2002). Predicting The Rolling Force in Hot Steel Rolling Mill using an Ensemble Model. In Proceedings of the Second IASTED International Conference on Artificial Intelligence and Applications (AIA '02) Benalmßdena, Spain. Calgary, Canada: ACTA Press, pages 143-148. [Abstract] [Prepublication PDF]
98.Rolfe, B., Y Frayman, P. Hodgson, and G. I. Webb (2002). Fault Detection in a Cold Forging Process Through Feature Extraction with a Neural Network. In Proceedings of the IASTED International Conference on Artificial Intelligence and Applications (AIA 2002) Benalmßdena, Spain. Calgary, Canada: ACTA Press, pages 155-159. [Abstract] [Pre-publication PDF]
97.Webb, G. I. and S. Zhang (2002). Removing Trivial Associations in Association Rule Discovery. In Proceedings of the First International NAISO Congress on Autonomous Intelligent Systems (ICAIS 2002) Geelong, Australia. Canada/The Netherlands: NAISO Academic Press. [Abstract] [Pre-publication PDF]
96.Frayman, Y, B. Rolfe, and G. I. Webb (2002). Improving an Inverse Model of Sheet Metal Forming by Neural Network Based Regression. In Proceedings of the Design Engineering Technical Conferences and Computer and Information in Engineering Conference (DETC'02/ASME 2002). Montreal, Canada: ASME Press, pages 1-8. [Abstract] [Prepublication PDF]
95.Frayman, Y, B. Rolfe, and G. I. Webb (2002). Solving Regression Problems using Competitive Ensemble Models. In B. McKay and J.K. Slaney (Eds.), Lecture Notes in Computer Science Vol. 2557: Proceedings of the 15th Australian Joint Conference on Artificial Intelligence (AI 02) Canberra, Australia. Berlin/Heidelberg: Springer, pages 511-522. [Abstract] [Prepublication PDF][Link to paper via Springerlink]
94.Pearce, J., G. I. Webb, R. Shaw, and B. Garner (2002). A Systemic Approach to the Database Marketing Process.. In Proceedings of the Australian and New Zealand Marketing Academy Conference (ANZMAC 02) Geelong, Australia. Geelong, Victoria: Deakin University (CD Rom), pages pp 2941-2948. [Abstract] [PDF]
93.Yang, Y. and G. I. Webb (2002). A Comparative Study of Discretization Methods for Naive-Bayes Classifiers. In T. Yamaguchi, A. Hoffmann, H. Motoda and P. Compton (Eds.), Proceedings of the 2002 Pacific Rim Knowledge Acquisition Workshop (PKAW'02) Tokyo, Japan. Tokyo: Japanese Society for Artificial Intelligence, pages 159-173. [Abstract] [PDF]
92.Webb, G. I. and D. Brain (2002). Generality is Predictive of Predication Accuracy. In T. Yamaguchi, A. Hoffmann, H. Motoda and P. Compton (Eds.), Proceedings of the 2002 Pacific Rim Knowledge Acquisition Workshop (PKAW'02) Tokyo, Japan. Tokyo: Japanese Society for Artificial Intelligence, pages 117-130. [Abstract] [PDF]
91.Wang, Z. and G. I. Webb (2002). A Heuristic Lazy Bayesian Rules Algorithm. In S.J Simoff, G.J Williams and M. Hegland (Eds.), Proceedings of the First Australasian Data Mining Workshop (AusDM02) Canberra, Australia. Sydney: University of Technology, pages 57-63. [Abstract] [PDF]
90.Webb, G. I., J. Boughton, and Z. Wang (2002). Averaged One-Dependence Estimators: Preliminary Results. In S.J Simoff, G.J Williams and M. Hegland (Eds.), Proceedings of the First Australasian Data Mining Workshop (AusDM02) Canberra, Australia. Sydney: University of Technology, pages 65-73. [Abstract] [PDF]
89.Webb, G. I., M. J. Pazzani, and D. Billsus (2001). Machine learning for user modeling. User Modeling and User-Adapted Interaction 11. Netherlands: Springer, pages 19-20. [Abstract] [Pre-publication PDF][Paper via Springerlink]
88.Webb, G. I. (2001). Discovering Associations with Numeric Variables. In F. Provost and R. Srikant (Eds.), Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)[short paper] San Francisco, CA. New York: The Association for Computing Machinery, pages 383-388. [Abstract] [Pre-publication PDF][Link to paper via ACM Portal]
87.Yang, Y. and G.I. Webb (2001). Proportional K-Interval Discretization for Naive-Bayes Classifiers. In L. DeRaedt and P. A. Flach (Eds.), Lecture Notes in Computer Science 2167: Proceedings of the 12th European Conference on Machine Learning (ECML'01) Freiburg, Germany. Berlin/Heidelberg: Springer-Verlag, pages 564-575. [Abstract] [Pre-Publication PDF][Paper via Springerlink]
86.Wang, Z., G. I. Webb, and H. Dai (2001). Implementation of Lazy Bayesian Rules in the Weka System. In Software Technology Catering for 21st Century: Proceedings of the International Symposium on Future Software Technology (ISFST2001) Zheng Zhou, China. Tokyo: Software Engineers Association, pages 204-208. [Abstract]
85.Webb, G. I. (2001). Candidate Elimination Criteria for Lazy Bayesian. In M. Stumptner, D. Corbett and M.J. Brooks (Eds.), Lecture Notes in Computer Science Vol. 2256: Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI'01) Adelaide, Australia. Berlin/Heidelberg: Springer, pages 545-556. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
84.Webb, G.I. and S. Zhang (2001). Further Pruning for Efficient Association Rule Discovery. In M. Stumptner, D. Corbett and M.J. Brooks (Eds.), Lecture Notes in Computer Science Vol. 2256: Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI'01) Adelaide, Australia. Berlin: Springer, pages 605-618. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
83.Webb, G. I. (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning 40(2). Netherlands: Springer, pages 159-196. [Abstract] [Pre-publication PDF][Paper via Springerlink]
82.Zheng, Z. and G. I. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning 41(1). Netherlands: Springer, pages 53-84. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
81.Smith, P. A. and G. I. Webb (2000). The Efficacy of a Low-Level Program Visualization Tool for Teaching Programming Concepts to Novice C Programmers. Journal of Educational Computing Research 22(2). Baywood Publishing, pages 187-215.[Pre-publication PDF][Link to paper via Baywood Publishing]
80.Webb, G. I. (2000). Efficient Search for Association Rules. In R. Ramakrishnan and S. Stolfo (Eds.), Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000) Boston, MA. New York: The Association for Computing Machinery, pages 99-107. [Abstract] [Pre-publication PDF][Link to paper via ACM Portal]
79.Webb, G. I., J. Wells, and Z. Zheng (1999). An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition. Machine Learning 35(1). Netherlands: Springer, pages 5-24. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
78.Webb, G. I. (1999). Decision Tree Grafting From The All Tests But One Partition. In T. Dean (Ed.), Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI 99) Stockholm, Sweden. San Francisco: Morgan Kaufmann, pages 702-707. [Abstract] [PDF][Reproduced with permision of IJCAI Inc.]
77.Zheng, Z., G. I. Webb, and K. M. Ting (1999). Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. In I. Bratko and S. Dzeroski (Eds.), Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99) Bled, Slovenia. San Francisco: Morgan Kaufmann, pages 493-502. [Abstract] [Pre-publication PDF]
76.Zheng, Z. and G.I. Webb (1999). Stochastic Attribute Selection Committees with Multiple Boosting: Learning More Accurate and More Stable Classifier Committees. In N. Zhong and L. Zhou (Eds.), Lecture Notes in Computer Science 1574: Methodologies for Knowledge Discovery and Data Mining - Proceedings of the Third Pacific-Asia Conference (PAKDD'99) Beijing, China. Berlin/Heidelberg: Springer-Verlag, pages 123-132. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
75.Newlands, D. and G. I. Webb (1999). Convex Hulls in Concept Induction. In N. Zhong and L. Zhou (Eds.), Lecture Notes in Computer Science 1574: Methodologies for Knowledge Discovery and Data Mining - Proceedings of the Third Pacific-Asia Conference (PAKDD'99) Beijing, China. Berlin/Heidelberg: Springer-Verlag, pages 306-316. [Abstract] [Link to paper via Springerlink]
74.Chiu, B. C. and G. I. Webb (1999). Dual-Model: An Architecture for Utilizing Temporal Information in Student Modeling. In G. Cumming, T. Okamoto and L. Gomez (Eds.), Proceedings of the Seventh International Conference on Computers in Education (ICCE '99), volume 1 Chiba, Japan.(Also appeared in the Proceedings of ACAI Workshop W03: Machine Learning in User Modeling, pp 46-53). Amsterdam: IOS Press, pages 111-118. [Abstract] [Pre-Publication PDF]
73.Smith, P. and G. I. Webb (1999). Evaluation of Low-Level Program Visualisation for Teaching Novice C Programmers. In G. Cumming, T. Okamoto and L. Gomez (Eds.), Proceedings of the Seventh International Conference on Computers in Education (ICCE '99), volume 2 Chiba, Japan. Amsterdam: IOS Press, pages 385-392. [Abstract] [Pre-publication PDF]
72.Ting, K.M. and Z. Zheng, & G. I. Webb (1999). Learning Lazy Rules to Improve the Performance of Classifiers. In F. Coenen and A. Macintosh (Eds.), Proceedings of the Nineteenth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence (ES'99) Peterhouse College, Cambridge, UK. New York: Springer, pages 122-131. [Abstract] [Pre-publication PDF]
71.Brain, D. and G. I. Webb (1999). On The Effect of Data Set Size on Bias And Variance in Classification Learning. In D. Richards, G. Beydoun, A. Hoffmann and P. Compton (Eds.), Proceedings of the Fourth Australian Knowledge Acquisition Workshop (AKAW '99) Sydney, Australia. Sydney: The University of New South Wales, pages 117-128. [Abstract] [Pre-publication PDF]
70.Webb, G. I. (1998). Preface to UMUAI Special Issue on Machine Learning for User Modeling. User Modeling and User-Adapted Interaction. 8(1). Netherlands: Kluwer Academic Publishers, pages 1-3, Springer (Netherlands).[Pre-publication PDF][Link to paper via Springerlink]
69.Chiu, B. C. and G. I. Webb (1998). Using Decision Trees For Agent Modelling: Improving Prediction Performance. User Modeling and User-Adapted Interaction 8(1-2). Netherlands: Springer, pages 131-152. [Abstract] [Pre-publication PDF][Paper via Springerlink]
68.Webb, G.I. and M. Kuzmycz (1998). Evaluation Of Data Aging: A Technique For Discounting Old Data During Student Modeling. In B.P. Goettl, H. M. Halff, C. Redfield and V. Shute (Eds.), Lecture Notes in Computer Science Vol. 1452: Proceedings of the Fourth International Conference on Intelligent Tutoring Systems (ITS '98) San Antonio, Texas. Berlin: Springer-Verlag, pages 384-393. [Abstract] [Pre-publication PDF][LNCS Volume via Springerlink]
67.Viswanathan, M. and G.I. Webb (1998). Classification Learning Using All Rules. In C. Nedellec and C. Rouveiro (Eds.), Lecture Notes in Computer Science 1398: Proceedings of the Tenth European Conference on Machine Learning (ECML'98) Chemnitz, Germany. Berlin/Heidelberg: Springer, pages 149-159. [Abstract] [Pre-publication PDF]
66.Smith, P. and G. I. Webb (1998). Overview of a Low-Level Program Visualisation Tool for Novice Programmers. In Proceedings of the Sixth International Conference on Computers in Education (ICCE '98) Beijing. Berlin: Springer-Verlag, pages 213-216. [Abstract] [Pre-publication PDF]
65.Zheng, Z., G. I. Webb, and K. M. Ting (1998). Integrating Boosting and Stochastic Attribute Selection Committees for Further Improving The Performance of Decision Tree Learning. In Proceedings of the Tenth IEEE International Conference on Tools with Artificial Intelligence (ICTAIĆ98) Taipei, Taiwan. Los Alamitos, CA: IEEE Computer Society Press, pages 216-223. [Abstract] [PDF][Link to paper via IEEE xplore]
64.Zheng, Z. and G. I. Webb (1998). Multiple Boosting: A Combination of Boosting and Bagging. In Proceedings of the 1998 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA'98) Las Vegas, Nevada. CSREA Press, pages 1133-1140. [Abstract] [Pre-publication PDF]
63.Webb, G. I. (1998). The Problem of Missing Values in Decision Tree Grafting. In G. Antoniou and J.K. Slaney (Eds.), Lecture Notes in Computer Science Vol. 1502: Advanced Topics in Artificial Intelligence, Selected Papers from the Eleventh Australian Joint Conference on Artificial Intelligence (AI '98) Brisbane, Australia. Berlin: Springer-Verlag, pages 273-283. [Abstract] [Pre-publication PDF]
62.Webb, G. I. and M. Pazzani (1998). Adjusted Probability Naive Bayesian Induction. In G. Antoniou and J.K. Slaney (Eds.), Lecture Notes in Computer Science Vol. 1502: Advanced Topics in Artificial Intelligence, Selected Papers from the Eleventh Australian Joint Conference on Artificial Intelligence (AI '98) Brisbane, Australia. Berlin: Springer-Verlag, pages 285-295. [Abstract] [Pre-publication PDF]
61.Zheng, Z. and G. I. Webb (1998). Stochastic Attribute Selection Committees. In G. Antoniou and J.K. Slaney (Eds.), Lecture Notes in Computer Science Vol. 1502: Advanced Topics in Artificial Intelligence, Selected Papers from the Eleventh Australian Joint Conference on Artificial Intelligence (AI '98) Brisbane, Australia. Berlin: Springer-Verlag, pages 321-332. [Abstract] [Pre-publication PDF]
60.Webb, G. I., B. C. Chiu, and M. Kuzmycz (1997). Comparative Evaluation of Alternative Induction Engines for Feature Based Modelling. International Journal of Artificial Intelligence in Education 8. NAmsterdam: IOS Press, pages 97-115. [Abstract] [Link to paper via IJAIED]
59.Webb, G. I. (1997). Decision Tree Grafting. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 97) Nagoya, Japan. San Francisco: Morgan Kaufmann, pages 846-851. [Abstract] [PDF][Reproduced with permision of IJCAI Inc.]
58.Chiu, B.C., G.I. Webb, and M. Kuzmycz (1997). A Comparison of First-Order and Zeroth-Order Induction for Input-Output Agent Modelling. In A. Jameson, C. Paris and C. Tasso (Eds.), Proceedings of the Sixth International Conference on User Modeling (UM'97) Chia Laguna, Sardinia. New York/Vienna: Springer, pages 347-358. [Abstract] [Paper via UM.org]
57.Chiu, B. C., G. I. Webb, and Z. Zheng (1997). Using Decision Trees for Agent Modelling: A Study on Resolving Conflicting Predictions. In A. Sattar (Ed.), Lecture Notes in Computer Science Vol. 1342: Proceedings of the Tenth Australian Joint Conference on Artificial Intelligence (AI'97) Perth, Australia. Berlin: Springer-Verlag, pages 349-358. [Abstract] [Pre-Publication PDF]
56.Chiu, B. C. and G.I. Webb (1997). Using C4.5 as an Induction Engine for Agent Modeling: An Experiment of Optimisation. In Proceedings (on-line) of The First Machine Learning for User Modeling Workshop (UM'97) Chia Laguna, Sardinia..[Pre-publication PDF][Link to paper via UM'97 site]
55.Webb, G. I. (1996). Further Experimental Evidence Against The Utility Of Occams Razor. Journal of Artificial Intelligence Research 4. Menlo Park, CA: AAAI Press, pages 397-417. [Abstract] [Link to paper via JAIR website]
54.Webb, G.I. and M. Kuzmycz (1996). Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agents Competencies. User Modelling and User-Adapted Interaction 5(2). Netherlands: Springer, pages 117-150. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
53.Webb, G. I. (1996). Integrating Machine Learning With Knowledge Acquisition Through Direct Interaction With Domain Experts. Knowledge-Based Systems 9. Elsevier, pages 253-266. [Abstract] [Pre-publication PDF][Paper via Science Direct]
52.Webb, G. I. (1996). Cost Sensitive Specialisation. In N.Y. Foo and R. Goebel (Eds.), Lecture Notes in Computer Science Vol. 1114. Topics in Artificial Intelligence: Proceedings of the Fourth Pacific Rim International Conference on Artificial Intelligence (PRICAI'96) Cairns, Australia. Berlin/Heidelberg: Springer-Verlag, pages 23-34. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
51.Webb, G. I. (1996). Inclusive Pruning: A New Class of Pruning Rule for Unordered Search and its Application to Classification Learning. In K. Ramamohanarao (Ed.), Australian Computer Science Communications Vol. 18 (1): Proceedings of the Nineteenth Australasian Computer Science Conference (ACSC'96) Royal Melbourne Insitute of Technology, Australia. Melbourne: ACS, pages 1-10. [Abstract] [PDF]
50.Webb, G.I. and J. Wells (1996). An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition Through Direct Interaction with Domain Experts. In P. Compton, R. Mizoguchi, H. Motada and T. Menzies (Eds.), Proceedings of the 1996 Pacific Knowledge Acquisition Workshop (PKAW'96) Coogee, Sydney, Australia. Sydney: UNSW Press, pages 170-189. [Abstract] [Pre-publication PDF]
49.Webb, G. I. (1996). A Heuristic Covering Algorithm Outperforms Learning All Rules. In Proceedings of Information, Statistics and Induction in Science (ISIS '96) Melbourne, Australia. Singapore: World Scientific, pages 20-30. [Abstract] [Pre-publication PDF]
48.Webb, G. I. (1995). OPUS: An Efficient Admissible Algorithm For Unordered Search. Journal of Artificial Intelligence Research 3. Menlo Park, CA: AAAI Press, pages 431-465. [Abstract] [Link to paper via JAIR website]
47.Newlands, D. and G. I. Webb (1995). Polygonal Inductive Generalisation System. In G. Forsyth and M. Ali (Eds.), Proceedings of the Eighth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE '95) Melbourne, Australia. Newark, NJ, USA: Gordon and Breach Science Publishers, Inc, pages 587-592. [Abstract] [Pre-publication PDF][Link to paper via ACM Portal]
46.Webb, G. I. and J. Wells (1995). Recent Progress in Machine-Expert Collaboration for Knowledge Acquisition. In X. Yao (Ed.), Proceedings of the Eighth Australian Joint Conference on Artificial Intelligence (AI'95) Canberra, Australia. Singapore: World Scientific, pages 291-298. [Abstract] [Prepublication PDF]
45.Smith, P. and G. I. Webb (1995). Transparency Debugging with Explanations for Novice Programmers. In M. Ducass‰ (Ed.), Proceedings of the Second International Workshop on Automated and Algorithmic Debugging (AADEBUG'95) Saint-Malo, France. IRISA-CNRS. [Abstract] [Pre-publication PDF]
44.Smith, P. and G. I. Webb (1995). Reinforcing a Generic Computer Model for Novice Programmers. In Proceedings of the Seventh Australian Society for Computers in Learning in Tertiary Education Conference (ASCILITE '95) Melbourne, Australia. Melbourne: ASCILITE. [Abstract] [Pre-publication PDF][Link to paper via ASCLITE]
43.Yip, S. and G. I. Webb (1994). Incorporating Canonical Discriminate Attributes in Classification Learning. In R. Elio (Ed.), Proceedings of the Tenth Biennial Canadian Artificial Intelligence Conference(AI-94) Banff, Canada. San Francisco: Morgan Kaufmann, pages 63-70. [Abstract] [Pre-publication PDF]
42.Webb, G. I. (1994). Recent Progress in Learning Decision Lists by Prepending Inferred Rules. In Proceedings of the Second Singapore International Conference on Intelligent Systems (SPICIS ć94) Singapore. Singapore: Asia Computer Weekly, pages B280-B285. [Abstract] [Pre-publication PDF]
41.Webb, G. I. (1994). Generality Is More Significant Then Complexity: Toward An Alternative To Occams Razor. In C. Zhang, J. Debenham and D. Lukose (Eds.), Artificial Intelligence: Sowing the Seeds for the Future, Proceedings of Seventh Australian Joint Conference on Artificial Intelligence (AI'94) Armidale,NSW, Australia. Singapore: World Scientific, pages 60-67. [Abstract] [Pre-publication PDF]
40.Yip, S. and G. I. Webb (1994). Empirical Function Attribute Construction in Classification Learning. In C. Zhang, J. Debenham and D. Lukose (Eds.), Artificial Intelligence: Sowing the Seeds for the Future, Proceedings of Seventh Australian Joint Conference on Artificial Intelligence (AI'94) Armidale,NSW, Australia. Singapore: World Scientific, pages 29-36. [Abstract] [Pre-publication PDF]
39.Webb, G. I. (1993). Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agents Competency. In P. Brna, S. Ohlsson and H. Pain (Eds.), Proceedings of the 1993 World Conference on Artificial Intelligence in Education (AI-ED'93) Edinburgh, Scotland. Also published in User Modeling and User-Adapted Interaction. 5: 117-150, 1996. Charlottesville, VA: AACE, pages 497-504. [Abstract] [Pre-publication PDF]
38.Webb, G. I. (1993). Systematic Search for Categorical Attribute-Value Data-Driven Machine Learning. In C. Rowles, H. Liu and N. Foo (Eds.), Proceedings of the Sixth Australian Joint Conference on Artificial Intelligence (AI'93) Melbourne, Australia. Singapore: World Scientific, pages 342-347. [Abstract] [Pre-publication PDF]
37.Webb, G.I. (1993). DLGref2: Techniques for Inductive Rule Refinement. In Proceedings of the 1993 IJCAI Workshop W16: Machine Learning and Knowledge Acquisition Chambery, France., pages 236-252. [Abstract] [Pre-publication PDF]
36.Webb, G. I. (1993). Control, Capabilities and Communication: Three Key Issues for Machine-Expert Collaborative Knowledge Acquisition. In N. Aussenac, G. Boy, B. Gaines, M. Linster, J.G. Ganascia and Y. Kodratoff (Eds.), Proceedings (Complement) of the Seventh European Workshop on Knowledge Acquisition for Knowledge-based Systems (EWKA'93) Toulouse, France. Springer-Verlag, pages 263-275. [Abstract] [Pre-publication PDF]
35.Webb, G. I. and N. Brkic (1993). Learning Decision Lists by Prepending Inferred Rules. In S. Sestito (Ed.), Proceedings of the AI 93 Workshop on Machine Learning and Hybrid Systems Melbourne, Australia., pages 6-10. [Abstract] [Pre-publication PDF]
34.Webb., G. I. and J. Agar (1992). Inducing Diagnostic Rules For Glomerular Disease With The DLG Machine Learning Algorithm. Artificial Intelligence in Medicine 4(6). Elsevier, pages 419-430. [Abstract] [Link to paper via Science Direct]
33.Agar, J. and G. I. Webb (1992). The Application Of Machine Learning To A Renal Biopsy Data-Base. Nephrology, Dialysis and Transplantation 7. Oxford UK: Oxford University Press, pages 472-478. [Abstract] [Link to paper via Oxford Journals On-line]
32.Kuzmycz, M. and G. I. Webb (1992). Evaluation of Feature Based Modelling in Subtraction. In C. Frasson, G. Gauthier and G. I. McCalla (Eds.), Lecture Notes in Computer Science Vol. 608: Proceedings of the Second International Conference on Intelligent Tutoring Systems (ITS'92) Montr‰al, Canada. Berlin: Springer-Verlag, pages 269-276. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
31.Yip, S. and G. I. Webb (1992). Function Finding in Classification Learning. In Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence (PRICAI '92) Seoul, Korea. Berlin: Springer-Verlag, pages 555-561. [Abstract] [Pre-publication PDF]
30.Yip, S. and G. I. Webb (1992). Discriminate Attribute Finding in Classification Learning. In A. Adams and L. Sterling (Eds.), Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence (AI'92) Hobart, Tas., Australia. Singapore: World Scientific, pages 374-379. [Abstract] [Pre-publication PDF]
29.Webb, G. I. (1992). Man-Machine Collaboration for Knowledge Acquisition. In A. Adams and L. Sterling (Eds.), Proceedings of the Fifth Australian Joint Conference on Artificial Intelligence (AI'92) Hobart, Tas., Australia. Singapore: World Scientific, pages 329-334. [Abstract] [Pre-publication PDF]
28.Smith, P. and G. I. Webb (1992). Recent progress in the Development of a Debugging Assistant for Computer Programs. In . B. Chia, R. Pennell and R. Sims (Eds.), A Future Promised: Proceedings of the Fifth Australian Society for Computers in Learning in Tertiary Education Conference (ASCILITE '92) Sydney, Australia., pages 351-356. [Abstract] [Pre-publication PDF]
27.Webb, G.I. and J. Agar (1991). The Application of Machine Learning to the Diagnosis of Glomerular Disease. In C. Sarmeinto (Ed.), Proceedings of the IJCAI Workshop W.15: Representing Knowledge in Medical Decision Support Systems Sydney, Australia., pages 8.1-8.8. [Abstract] [Pre-publication PDF]
26.Webb, G.I. (1991). An Attribute-Value Machine Learning Approach To Student Modelling. In J. Kay and A. Quilici (Eds.), Proceedings of the IJCAI Workshop W.4: Agent Modelling for Intelligent Interaction Sydney, Australia., pages 128-136. [Abstract] [Pre-publication PDF]
25.Webb, G. I. (1991). Einstein: An Interactive Inductive Knowledge-Acquisition Tool. In Proceedings of the Sixth Banff Knowledge Acquisition for Knowledge-Based Systems Workshop Banff, Canada., pages (36)1-16. [Abstract]
24.Webb, G. I. (1991). Data Driven Inductive Refinement of Production Rules. In R. Quinlan (Ed.), Proceedings of the First Australian Workshop on Knowledge Acquisition for Knowledge-Based Systems (AKAW '91) Pokolbin, NSW, Australia. Sydney: University of Sydney Press., pages 44-52. [Abstract] [Pre-publication PDF]
23.Kuzmycz, M. and G. I. Webb (1991). Modelling Elementary Subtraction: Intelligent Warfare Against Bugs. In R. Godfrey (Ed.), Simulation & Academic Gaming in Tertiary Education, The Proceedings of the Eighth Annual Conference of ASCILITE (ASCILITE '91) Launceston, TAS, Australia. Launceston: University of Tasmania, pages 367-376. [Abstract] [Pre-publication PDF]
22.Smith, P. and G. I. Webb (1991). Debugging Using Partial Models. In R. Godfrey (Ed.), Simulation & Academic Gaming in Tertiary Education, The Proceedings of the Eighth Annual Conference of ASCILITE (ASCILITE '91) Launceston, TAS, Australia. Launceston: University of Tasmania, pages 581-590. [Abstract] [Pre-publication PDF]
21.Webb, G. I. (1991). Inside the Unification Tutor: The Architecture of an Intelligent Educational System. In R. Godfrey (Ed.), Simulation & Academic Gaming in Tertiary Education, The Proceedings of the Eighth Annual Conference of ASCILITE (ASCILITE '91). Launceston: University of Tasmania, pages 677-684. [Abstract] [Pre-publication PDF]
20.Surruwerra, L. Sanzogni,F. and G.I. Webb (1990). Improving the Efficiency of Rule Based Expert Systems by Rule Activation. Journal of Experimental and Theoretical Artificial Intelligence 2. Taylor and Francis, pages 369-380. [Abstract] [Pre-publication PDF][Journal of Experimental & Theoretical Artificial Intelligence]
19.Webb, G. I., G. Cumming, T. Richards, and K-K. Yum (1990). Educational Evaluation of Feature Based Modelling in a Problem Solving Domain. In R. Lewis and S. Otsuki (Eds.), Proceedings of the IFIP TC3 International Conference on Advanced Research on Computers in Education (ARCE'90) Tokyo, Japan. Amsterdam: Elsevier, pages 101-108. [Abstract] [Pre-publication PDF]
18.Webb, G. I. (1990). Rule Optimisation and Theory Optimisation: Heuristic Search Strategies for Data-Driven Machine Learning. In H. Motada, R. Mizoguchi, J. Boose and B. Gaines (Eds.), Proceedings of the First Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW'90) Kyoto & Hatoyama, Japan. Tokyo: IOS Press, pages 219-232. [Abstract] [Pre-publication PDF]
17.Webb, G. I. (1989). Courseware Abstraction: Reducing Development Costs While Producing Qualitative Improvements in CAL. Journal of Computer Assisted Learning 5. Blackwell Publishing, pages 103-113. [Abstract] [Pre-publication PDF][Link to Blackwell Publishing and the Journal of Computer Assisted Learning]
16.Webb, G. I. (1989). A Machine Learning Approach to Student Modelling. In Proceedings of the Third Australian Joint Conference on Artificial Intelligence (AI 89) Melbourne, Australia., pages 195-205. [Abstract] [Pre-publication PDF]
15.Webb, G. I., G. Cumming, T. Richards, and K-K. Yum (1989). The Unification Tutor: An Intelligent Educational System in the Classroom. In G. Bishop and J. Baker (Eds.), Proceedings of the Seventh Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE '89) Gold Coast, QLD, Australia. Gold Coast: Bond University, pages 408-420. [Abstract] [Pre-publication PDF]
14.Webb, G. I. (1988). A Knowledge-Based Approach To Computer-Aided Learning. International Journal of Man-Machine Studies 29. Academic Press, pages 257-285. [Abstract] [Pre-publication PDF][Now known as Int. Journal of Human-Computer Studies]
13.Richards, T., G. I. Webb, and N. Craske (1988). Object-oriented Control for Intelligent Computer Assisted Learning Systems. In P. Ercoli and R. Lewis (Eds.), Proceedings of the IFIP TC3 Working Conference on Artificial Intelligence Tools in Education Frascati, Italy. North-Holland, Amsterdam: Elsevier, pages 203-219. [Abstract] [Pre-publication PDF]
12.Webb, G. I. (1988). Techniques for Efficient Empirical Induction. In C. J. Barter and M. J. Brooks (Eds.), Lecture Notes in Artificial Intelligence Vol. 406: Proceedings of the Second Australian Joint Conference on Artificial Intelligence (AI'88) Adelaide, S.A., Australia. Berlin: Springer-Verlag, pages 225-239. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
11.Webb, G. I. (1988). Cognitive Diagnosis Using Student Attributions. In K. Fielden, F. Hicks and N. Scott (Eds.), Computers in Learning in Tertiary education: Proceedings of the Sixth Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE-88) Canberra, Australia., pages 502-514. [Abstract] [Pre-publication PDF]
10.Webb, G. I. (1987). Domain and Tutoring Knowledge in Computer-Aided Learning. In J. Gero and F. Sudweeks (Eds.), Proceedings of the First Australian Joint Conference on Artificial Intelligence (AI'87) Sydney, Australia. Sydney: The University of Sydney Printing Service, pages 488-502. [Abstract] [Pre-publication PDF]
9.Webb, G. I. (1987). Generative CAL and Courseware Abstraction. In J. Barrett and J. Hedberg (Eds.), Using computers intelligently in Tertiary Education: Proceedings of the Fifth Annual Conference of the Australian Society for Computers in Learning in Tertiary Education (ASCILITE-87) Sydney, Australia., pages 257-285. [Abstract] [Pre-publication PDF]
8.Webb, G.I. (1986). Knowledge Based Flow of Control in Computer-Aided Learning. In Proceedings of the First Australian Artificial Intelligence Congress (1AAIC'86) Melbourne, Australia., pages B: 1-7. [Abstract] [Pre-publication PDF]
7.Webb, G. I. (1986). The Domain-Analysis Based Instruction System. In G. Bishop and W. vanLint (Eds.), Proceedings of the Fourth Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'86) Adelaide, Australia. Adelaide: University of Adelaide, pages 295-302. [Abstract] [Pre-publication PDF]
6.Richards, T. and G. I. Webb (1985). ECCLES An Expert System for CAL. In H. Garrett (Ed.), Proceedings of the Tenth Western Educational Computing Conference (WECC'85) Oakland, CA. North Hollywood, CA: : Western Periodicals Company, pages 151-157. [Abstract] [Pre-publication PDF]
5.Webb, G. I. (1985). Student Control Under the Feature-Network Based Courseware Design Methodology. In J.A. Bowden & S. Lichtenstein (Ed.), Student Control of Learning: Proceedings of the Third Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'85) Melbourne, Australia. Melbourne: University of Melbourne, pages 27-34.
4.Richards, T., G. I. Webb, and S. Bodnar (1984). ECCLES An Intelligent C.A.L. System. In R. Russell (Ed.), Proceedings of the Second Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'84) Brisbane, Australia. Brisbane: University of Queensland, pages 232-235.
3.Webb, G. I. (1984). A Methodology for Intermediate Level Knowledge Representation in CAL. In R. Russell (Ed.), Proceedings of the Second Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'84), Brisbane, Australia. Brisbane: University of Queensland, pages 288-303.
2.Richards, T., R. Hooke, and G. I. Webb (1983). Automatic Authoring of Complex and Analytical Question-Answer Lessons. In R. Russell (Ed.), Proceedings of the First Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'83) Brisbane, Australia. Brisbane: University of Queensland, pages 282-293.
1.Webb, G. I. (1983). A Computer Based Language Instructional Expert. In R. Russell (Ed.), Proceedings of the First Annual Computer-Assisted Learning in Tertiary Education Conference (CALITE'83) Brisbane, Australia. Brisbane: University of Queensland, pages 391-402.