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Note: Links lead to the DBLP on the Web. Geoffrey I. Webb Dhananjay R. Thiruvady , Geoffrey I. Webb: Mining Negative Rules Using GRD. PAKDD 2004 : 161-165 Zhihai Wang , Geoffrey I. Webb, Fei Zheng : Selective Augmented Bayesian Network Classifiers Based on Rough Set Theory. PAKDD 2004 : 319-328 Ying Yang , Geoffrey I. Webb: On Why Discretization Works for Naive-Bayes Classifiers. Australian Conference on Artificial Intelligence 2003 : 440-452 Zhihai Wang , Geoffrey I. Webb, Fei Zheng : Adjusting Dependence Relations for Semi-Lazy TAN Classifiers. Australian Conference on Artificial Intelligence 2003 : 453-465 Shane M. Butler , Geoffrey I. Webb, Rob A. Lewis : A Case Study in Feature Invention for Breast Cancer Diagnosis Using X-Ray Scatter Images. Australian Conference on Artificial Intelligence 2003 : 677-685 Geoffrey I. Webb, Shane M. Butler , Douglas A. Newlands : On detecting differences between groups. KDD 2003 : 256-265 Hongbo Shi , Zhihai Wang , Geoffrey I. Webb, Houkuan Huang : A New Restricted Bayesian Network Classifier. PAKDD 2003 : 265-270 Ying Yang , Geoffrey I. Webb: Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers. PAKDD 2003 : 501-512 Chengqi Zhang , Shichao Zhang , Geoffrey I. Webb: Identifying Approximate Itemsets of Interest in Large Databases. Appl. Intell. 18 (1): 91-104 (2003) Yakov Frayman , Bernard F. Rolfe , Geoffrey I. Webb: Solving Regression Problems Using Competitive Ensemble Models. Australian Joint Conference on Artificial Intelligence 2002 : 511-522 Zhihai Wang , Geoffrey I. Webb: Comparison of Lazy Bayesian Rule and Tree-Augmented Bayesian Learning. ICDM 2002 : 490-497 James E. Pearce , Geoffrey I. Webb, Robin N. Shaw , Brian Garner : Experimentation and Self Learning in Continuous Database Marketing. ICDM 2002 : 775-778 Ying Yang , Geoffrey I. Webb: Non-Disjoint Discretization for Naive-Bayes Classifiers. ICML 2002 : 666-673 Damien Brain , Geoffrey I. Webb: The Need for Low Bias Algorithms in Classification Learning from Large Data Sets. PKDD 2002 : 62-73 Geoffrey I. Webb: Candidate Elimination Criteria for Lazy Bayesian Rules. Australian Joint Conference on Artificial Intelligence 2001 : 545-556 Songmao Zhang , Geoffrey I. Webb: Further Pruning for Efficient Association Rule Discovery. Australian Joint Conference on Artificial Intelligence 2001 : 605-618 Ying Yang , Geoffrey I. Webb: Proportional k-Interval Discretization for Naive-Bayes Classifiers. ECML 2001 : 564-575 Geoffrey I. Webb: Discovering associations with numeric variables. KDD 2001 : 383-388 Geoffrey I. Webb, Michael J. Pazzani , Daniel Billsus : Machine Learning for User Modeling. User Model. User-Adapt. Interact. 11 (1-2): 19-29 (2001) Geoffrey I. Webb: Efficient search for association rules. KDD 2000 : 99-107 Geoffrey I. Webb: MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning 40 (2): 159-196 (2000) Zijian Zheng , Geoffrey I. Webb: Lazy Learning of Bayesian Rules. Machine Learning 41 (1): 53-84 (2000) Zijian Zheng , Geoffrey I. Webb, Kai Ming Ting : Lazy Bayesian Rules: A Lazy Semi-Naive Bayesian Learning Technique Competitive to Boosting Decision Trees. ICML 1999 : 493-502 Geoffrey I. Webb: Decision Tree Grafting From the All Tests But One Partition. IJCAI 1999 : 702-707 Zijian Zheng , Geoffrey I. Webb: Stochastic Attribute Selection Committees with Aultiple Boosting: Learning More Accurate and More Stable Classifer Committees. PAKDD 1999 : 123-132 Douglas A. Newlands , Geoffrey I. Webb: Convex Hulls in Concept Induction. PAKDD 1999 : 306-316 Geoffrey I. Webb, Jason Wells , Zijian Zheng : An Experimental Evaluation of Integrating Machine Learning with Knowledge Acquisition. Machine Learning 35 (1): 5-23 (1999) Geoffrey I. Webb: The Problem of Missing Values in Decision Tree Grafting. Australian Joint Conference on Artificial Intelligence 1998 : 273-283 Geoffrey I. Webb, Michael J. Pazzani : Adjusted Probability Naive Bayesian Induction. Australian Joint Conference on Artificial Intelligence 1998 : 285-295 Zijian Zheng , Geoffrey I. Webb: Stochastic Attribute Selection Committees. Australian Joint Conference on Artificial Intelligence 1998 : 321-332 Murlikrishna Viswanathan , Geoffrey I. Webb: Classification Learning Using All Rules. ECML 1998 : 149-159 Geoffrey I. Webb, Mark Kuzmycz : Evaluation of Data Aging: A Technique for Discounting Old Data During Student Modeling. Intelligent Tutoring Systems 1998 : 384-393 Geoffrey I. Webb: Preface to UMUAI Special Issue on Machine Learning for User Modeling. User Model. User-Adapt. Interact. 8 (1-2): 1-3 (1998) Bark Cheung Chiu , Geoffrey I. Webb: Using Decision Trees for Agent Modeling: Improving Prediction Performance. User Model. User-Adapt. Interact. 8 (1-2): 131-152 (1998) Bark Cheung Chiu , Geoffrey I. Webb, Zijian Zheng : Using Decision Trees for Agent Modelling: A Study on Resolving Confliction Predictions. Australian Joint Conference on Artificial Intelligence 1997 : 349-358 Geoffrey I. Webb: Decision Tree Grafting. IJCAI (2) 1997 : 846-851 Geoffrey I. Webb: Cost-Sensitive Specialization. PRICAI 1996 : 23-34 Geoffrey I. Webb: Further Experimental Evidence against the Utility of Occam's Razor. JAIR 4 : 397-417 (1996) Philip A. Smith , Geoffrey I. Webb: Transparency Debugging with Explanations for Novice Programmers. AADEBUG 1995 : 105-118 Douglas A. Newlands , Geoffrey I. Webb: Polygonal Inductive Generalisation System. IEA/AIE 1995 : 587-592 Geoffrey I. Webb: OPUS: An Efficient Admissible Algorithm for Unordered Search. JAIR 3 : 431-465 (1995) Geoffrey I. Webb, Mark Kuzmycz : Feature Based Modelling: A Methodology for Producing Coherent, Consistent, Dynamically Changing Models of Agents' Competencies. User Model. User-Adapt. Interact. 5 (2): 117-150 (1995) Mark Kuzmycz , Geoffrey I. Webb: Evaluation of Feature Based Modelling in Subtraction. Intelligent Tutoring Systems 1992 : 269-276 Geoffrey I. Webb: Techniques for Efficient Empirical Induction. Australian Joint Conference on Artificial Intelligence 1988 : 225-239 Geoffrey I. Webb: A Knowledge-Based Approach to Computer-Aided Learning. International Journal of Man-Machine Studies 29 (3): 257-285 (1988) 1 [ 27 ] 2 [ 32 ] 3 [ 40 ] [ 41 ] 4 [ 11 ] [ 12 ] 5 [ 36 ] 6 [ 34 ] 7 [ 39 ] 8 [ 3 ] [ 4 ] [ 14 ] 9 [ 41 ] 10 [ 6 ] [ 20 ] [ 40 ] 11 [ 17 ] [ 27 ] 12 [ 34 ] 13 [ 36 ] 14 [ 34 ] 15 [ 39 ] 16 [ 7 ] 17 [ 45 ] 18 [ 23 ] 19 [ 15 ] 20 [ 35 ] [ 39 ] [ 42 ] [ 44 ] 21 [ 19 ] 22 [ 29 ] [ 33 ] [ 38 ] [ 43 ] 23 [ 37 ] 24 [ 37 ] 25 [ 30 ] 26 [ 42 ] [ 44 ] 27 [ 11 ] [ 16 ] [ 19 ] [ 21 ] [ 23 ] [ 24 ]  ©2004 Association for Computing Machinery |
