Selected publications by Charles X. Ling

Last update: Dec 2011.

Book chapters

  1. C.X. Ling and V.S. Sheng. Cost-sensitive Learning and the Class Imbalanced Problem. In Encyclopedia of Machine Learning. C. Sammut (Ed.). Springer. 2011. (pdf preprint)

  2. V.S. Sheng and C.X. Ling. Cost-sensitive Learning. In Encyclopedia of Data Warehousing and Mining, 2nd Edition. J. Wang (Ed.). 2009.

  3. W. Lam, C.K. Leung, and C.X. Ling. Learning via Prototype Generation and Filtering. In Instance Selection and Generation for Data Mining, Edited by H. Liu and H. Motoda. Pages 227 - 244. Kluwer Academic Publishers, 2001.

  4. C.X. Ling and C. Li. Applying Data Mining to Direct Marketing. In Electronic Commerce Technology Trends: Challenges and Opportunities. W. Kou and Y. Yesha, editors. Pages 185 - 198. IBM Press. 2000.

Papers published in peer-reviewed journals

  1. J. Du, C.X. Ling, and Z. Zhou. When Does Co-Training Work in Real Data? IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 23(5), 788-799, May 2011. (pdf preprint)

  2. J. Du, C.X. Ling. Asking Generalized Queries to Domain Experts to Improve Learning. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), Special Issue on Domain Driven Data Mining, 2011. To appear. (pdf preprint)

  3. J. Liu, Y. Chen, C. Miao, J. Xie, C.X. Ling, and W. Gao. Semi-Supervised Learning in Reconstructed Manifold Space for 3D Caricature Generation. Computer Graphics Forum. 2009.

  4. J. Zhang, S. He, C.X. Ling, et al. PeakSelect: Preprocessing Tandem Mass Spectra for Better Peptide Identification. Rapid Communications in Mass Spectrometry, 22(8), Pages 1203-1212, 2008.

  5. Q. Yang, J. Yin, C.X. Ling, and R. Pan. Extracting Actionable Knowledge from Decision Trees. IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 19(1), 2007. Pages 43-56.

  6. C.X. Ling and S. Sheng. A Comparative Study of Cost-Sensitive Classifiers. Chinese Journal of Computers, 30(8), pp 1203-1212, 2007.

  7. D. Li, W. Gao, C.X. Ling, at al. An Open Source Toolbox to Index Protein Databases for High-throughput Proteomics. Bioinformatics, 22(20), pp 2572-2573, 2006.

  8. Y. Tian, Q. Yang, T. Huang, C.X. Ling, and W. Gao. Learning Contextual Dependency Network Models for Link-Based Classification. IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 18, Issue 11, pp 1482 - 1496. 2006.

  9. Y. Han, W. Lam, and C.X. Ling. Customized Generalization of Support Patterns for Classification. IEEE Transactions on Systems, Man and Cybernetics, Volume 36, Issue 6, pp 1306 - 1318. 2006.

  10. C.X. Ling, S. Sheng, Q. Yang. Test Strategies for Cost-Sensitive Decision Trees. IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 18, Number 8, pp. 1055-1067, 2006. (pdf)

  11. Q. Yang., C.X. Ling, X. Chai, and R. Pan. Test-Cost Sensitive Classification on Data with Missing Values. IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 18, Number 5, 2006. Pages: 626 - 638.

  12. C.X. Ling, Q. Yang. Discovering Classification from Data of Multiple Sources. International Journal of Data Mining and Knowledge Discovery. Volume 12 , Issue 2-3, 2006. Pages 181 - 201.

  13. S. Zhang, Z. Qin, C.X. Ling, S. Sheng. "Missing is Useful": Missing Values in Cost-sensitive Decision Trees. IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 17 , Issue 12, 1689-1693, 2005. (pdf file)

  14. C.X. Ling, W. Noble, Q. Yang, Introduction to the Special Issue - Machine Learning for Bioinformatics (Part 2). IEEE Transactions on Computational Biology and Bioinformatics, Vol. 2, No. 3, pp 177-178. 2005.

  15. C.X. Ling, W. Noble, Q. Yang, Introduction to the Special Issue - Machine Learning for Bioinformatics. IEEE Transactions on Computational Biology and Bioinformatics, Vol. 2, No. 2, pp 81-82. 2005. (pdf file)

  16. D. Li, Y. Fu, R. Sun, C.X. Ling, et al. pFind: A Novel Database-Searching Software System for Automated Peptide and Protein Iden-tification via Tandem Mass Spectrometry. Bioinformatics, 21(13): 3049-3050. 2005.

  17. J. Huang and C.X. Ling. Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering (TKDE), 17(3): 299-310. 2005.

  18. Y. Fu, Q. Yang, R. Sun, D. Li, R. Zeng, C.X. Ling, and W. Gao. Exploiting the Kernel Trick to Correlate Fragment Ions for Peptide Identification via Tandem Mass Spectrometry. Bioinformatics, vol 20, pages 1948-1954. 2004. (pdf file)

  19. H. Zhang and C.X. Ling. Numerical Mapping and Learnability of Naive Bayes. Applied Artificial Intelligence. Vol. 17 No. 5-6, pages 507-518, 2003.

  20. C.X. Ling and H. Zhang. The Representational Power of Discrete Bayesian Networks. Journal of Machine Learning Research (journal website). Vol 3:709-721, 2002. (pdf file)

  21. C.X. Ling, J. Gao, H. Zhang, W. Qian, H. Zhang. Improving Encarta Search Engine Performance by Mining User Logs. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI). Volume 16, Number 8, Pages 1101-1116, 2002. (pdf file)

  22. C.X. Ling and N. Cercone. Editorial Notes. Special Issue of International Journal of Foundations of Computer Science (IJFCS) on Mining the Web. pp. 473-476, Volume 13, Number 4, August 2002. (PS file)

  23. Y. Chen, W. Gao, T. Zhu, C.X. Ling, Learning Prosodic Patterns for Mandarin Speech Synthesis. Journal of Intelligent Information System, pp. 95-109, Volume 19, Number 1, 2002.

  24. W. Lam, C.K. Keung and C.X. Ling, Learning Good Prototypes for Classification Using Filtering and Abstraction of Instances. Pattern Recognition. Vol. 35, No. 7, pp. 1491-1506, July 2002. (PDF Preprint file)

  25. R. Sun and C.X. Ling. Computational cognitive modeling, the source of power and other related issues. Artificial Intelligence Magazine, pages 113 - 120. Summer 1998. (PS file)

  26. C.X. Ling, J. J. Parry and H. Wang. Setting attribute weights for nearest neighbour learning algorithms using C4.5. International Journal of Pattern Recognition and Artificial Intelligence, 11(3): 405 - 415. 1997. (preprint PS file)

  27. C.X. Ling and H. Wang. Computing optimal attribute weight settings for nearest neighbor algorithms. Journal of Artificial Intelligence Review, 11: 255 - 272. 1997. (PS file)

  28. W.C. Schmidt and C.X. Ling. A decision-tree model of balance scale development. Machine Learning, 24: 203 - 230. 1996. (PS file)

  29. C.X. Ling. Overfitting and generalization in learning discrete patterns. Neurocomputing: An International Journal, 8: 341-347. 1995. (PS file)

  30. C.X. Ling. Introducing new predicates to model scientific revolution. International Studies in the Philosophy of Science, 9(1): 19 - 36. 1995. (PS file)

  31. C.X. Ling and M. Valtorta. Refinement of rule bases with uncertainty via reduction. International Journal of Approximate Reasoning, 13(2): 95 - 126. 1995.

  32. C.X. Ling. Learning the past tense of English verbs: the Symbolic Pattern Associator vs. connectionist models. Journal of Artificial Intelligence Research, 1:209 - 229, 1994. (PS file)

  33. C.X. Ling and M. Marinov. A symbolic model of the nonconscious acquisition of information. Cognitive Science, 18(4): 595 - 621. 1994. (PS file)

  34. C.X. Ling and R. Buchal. Learning to control dynamic systems: A progressive quantization approach, Adaptive Behavior, 3(1): 29 - 49, 1994. (PS file)

  35. C.X. Ling and M. Marinov. Answering the connectionist challenge. Cognition, 49(3):235 - 290, 1993. (PS file)


Papers published in peer-reviewed conference proceedings

  1. K. Da, X. Li, and C.X. Ling. A New Search Engine Integrating Hierarchical Browsing and Keyword Search. The International Joint Conference on Artificial Intelligence (IJCAI 2011). (pdf) 2011.

  2. J. Du, and C.X. Ling. Asking Generalized Queries with Minimum Cost. Proceedings of The 2011 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). 2011.

  3. J. Du, and C.X. Ling. Active Teaching for Inductive Learners. P roceedings of 2011 SIAM International Conference on Data Mining (SDM). 2011.

  4. J. Du, and C.X. Ling. Asking Generalized Queries to Ambiguous Oracle. Proceedings of the European Conference on Machine Learning (ECML/PKDD 2010). 2010.

  5. J. Du, and C.X. Ling. Active Learning with Human-Like Noisy Oracle. The 10th IEEE International Conference on Data Mining (ICDM), 2010.

  6. E.A. Ni and C.X. Ling. Supervised Learning with Minimal Effort. Proceedings of The 2010 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). 2010.

  7. J. Du, C.X. Ling, and E.A. Ni. Adapting Cost-sensitive Learning for Reject Option. Proceedings of ACM Conference on Information and Knowledge Management (CIKM 2010). 2010.

  8. J. Du and C.X. Ling. Asking Generalized Queries in Active Learning. Proceedings of IEEE International Conference on Data Mining (ICDM 2009). (Top conference in data mining; full paper with acceptance rate of 1/10) (pdf)

  9. J. Du and C.X. Ling. Co-Training on Handwritten Digit Recognition. Proceedings of Canadian Conference on AI, 203-206, 2009.

  10. W. Gong, Z. Cai, C.X. Ling, and J. Du. Hybrid differential evolution based on fuzzy C-means clustering. Proceedings of Genetic and Evolutionary Computation Conference (GECCO), 523-530, 2009.

  11. C.X. Ling, J. Du, and Z-H Zhou. When does Co-training Work in Real Data? Proceedings of The 2009 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). 596-603. 2009. (Major conference in data mining). (pdf)

  12. W. Gong, Z. Cai, C.X. Ling, and Bo Huang. A Point Symmetry-Based Automatic Clustering Approach Using Differential Evolution. ISICA 2009, 151-162, 2009.

  13. C.X. Ling. From Machine Learning to Child Learning. The International Conference on Advanced Data Mining and Applications (ADMA 2009), 2009. (Abstract for the invited keynote speeach)

  14. C.X. Ling and J. Du. Active Learning with Direct Query Construction. 2008 ACM SIGKDD Conference (KDD'2008)}. (Top conference in data mining; full paper with acceptance rate: under 1/5) (pdf)

  15. J. Su, H. Zhang, C.X. Ling and S. Matwin. Discriminative Parameter Learning for Bayesian Networks. Proceedings of 2008 International Conference on Machine Learning (ICML'2008)}. 2008. (Top conference in machine learning) (pdf)

  16. J. Huang, C.X. Ling, H. Zhang, S. Matwin. Proper Model Selection with Significance Test Proceedings of the European Conference on Machine Learning (ECML-2008), 2008. (Top conference in machine learning; full paper; acceptance rate: under 1/5). (pdf)

  17. B. Wang, B. Spencer, C.X. Ling and H. Zhang Semi-Supervised Self-Training for Sentence Subjectivity Classification. Proceedings of 2008 Canadian Conference on Artificial Intelligence (AI'2008), 2008.

  18. V.S. Sheng and C.X. Ling. Partial Example Acquisition in Cost-Sensitive Learning. 2007 ACM SIGKDD Conference (KDD'2007). To appear. (Top KDD conference; full paper, with acceptance rate: 1/12).

  19. J. Yan and C.X. Ling. Machine Learning for Stock Selection. 2007 ACM SIGKDD Conference (KDD'2007). To appear. (Top KDD conference; Industrial and Government Track)

  20. J. Huang and C.X. Ling. Constructing New and Better Evaluation Measures for Machine Learning. The Twentieth International Joint Conference on Artificial Intelligence (IJCAI 2007). (Top conference in AI) (pdf file)

  21. V.S. Sheng and C.X. Ling. Roulette Sampling for Cost-Sensitive Learning. Proceedings of the European Conference on Machine Learning (ECML-2007), 2007.

  22. J. Du, Z. Cai, and C.X. Ling. Cost-sensitive Decision Trees with Pre-pruning. Proceedings of 2007 Canadian Conference on Artificial Intelligence (AI'2007), 2007.

  23. C. Huang, Y. Tian, T. Huang, C.X. Ling, and Z. Zhou. Extracting Keyphrases using Semantic Networks Structure Analysis. The 2006 IEEE International Conference on Data Mining (ICDM'2006). (Regular paper, acceptance rate: 1/10).

  24. J. Huang and C.X. Ling. Constructing Ensembles for Better Ranking. Proceedings of The 2006 IEEE International Conference on Data Mining (ICDM'2006). (Short paper, acceptance rate: 1/5).

  25. W. Gong, Z. Cai, and C.X. Ling. A Fast and Robust Differential Evolution Based on Orthogonal Design. Proceedings of 2006 Australian Joint Conference on Artificial Intelligence. 2006.

  26. C.X. Ling, V.S. Sheng, T. Bruckhaus, and N.H. Madhavji. Maximum Profit Mining and Its Application in Software Development. Proceedings of The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'06) (Industrial Applications Track), Pages 929-934, 2006. (Top conference in data mining)

  27. V.S. Sheng and C.X. Ling. Feature Value Acquisition in Testing: A Sequential Batch Test Algorithm. Proceedings of 2006 International Conference on Machine Learning (ICML'2006), Pages 809-816, 2006. (pdf file)

  28. V.S. Sheng, C.X. Ling, A. Ni, and S. Zhang. Cost-Sensitive Test Strategies. Proceedings of the Twenty-first National Conference on Artificial Intelligence (AAAI-06), 2006. (Top AI Conference) (pdf file)

  29. V.S. Sheng and C.X. Ling. Thresholding for Making Classifiers Cost-sensitive. Proceedings of the Twenty-first National Conference on Artificial Intelligence (AAAI-06), 2006. (Top AI Conference) (pdf file)

  30. R. Yan, J. Nuttall, C.X. Ling. Application of Machine Learning to Short-Term Equity Return Prediction. In 2006 SIAM conference on Financial Mathematics and Engineering. 2006.

  31. R. Yan, J. Nuttall, C.X. Ling. Can Machine Learning Challenge The Efficient Market Hypothesis? In 2006 Financial Management Association International (FMA) Annual Meeting. 2006.

  32. C.X. Ling, S. Sheng, T. Bruckhaus, and N.H. Madhavji. Predicting Software Escalations with Maximum ROI. Proceedings of The 2005 IEEE International Conference on Data Mining (ICDM'2005) pp. 717-720, 2005. (Acceptance rate: 1/7). (pdf file)

  33. J. Huang and C.X. Ling. Dynamic Ensemble Re-Construction for Better Ranking. Proceedings of The 2005 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2005. (Acceptance rate: 1/7).

  34. S. Sheng, C.X. Ling, & Q. Yang. Simple Test Strategies for Cost-Sensitive Decision Trees. Proceedings of the 16th European Conference on Machine Learning (ECML). pp. 365-376, 2005. (Regular paper, Acceptance rate: 1/9.3). (pdf file)

  35. S. Sheng & C.X. Ling. Hybrid Cost-sensitive Decision Tree. Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). 2005. (Regular paper, Acceptance rate: 1/7).

  36. J. Huang, C.X. Ling, Rank Measures for Ordering. Proceedings of 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2005.

  37. J. Huang, C.X. Ling, Dynamic Ensemble Re-Construction for Better Ranking. Proceedings of 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), 2005. (pdf file)

  38. X. Chai, L. Deng, Q. Yang, and C.X. Ling. Test-Cost Sensitive Naive Bayes Classification. Proceedings of The 2004 IEEE International Conference on Data Mining (ICDM'2004). (Regular paper, acceptance rate: 1/9.) (pdf file)

  39. T. Bruckhaus, C.X. Ling, N.H. Madhavji, S. Sheng. Software Escalation Prediction with Data Mining Workshop on Predictive Software Models (PSM 2004), A STEP 2004 workshop, 2004. (pdf file)

  40. C.X. Ling, Q. Yang, J. Wang, and S. Zhang. Decision Trees with Minimal Costs. Proceedings of 2004 International Conference on Machine Learning (ICML'2004), 2004. To appear. (pdf file)

  41. Y. Fu, Q. Yang, C.X. Ling, at el. A Kernel-based Case Retrieval Algorithm with Application to Bioinformatics. PRICAI 2004 (LNAI 3157), pp. 544 - 553, 2004.

  42. J. Zheng, C.X. Ling, Z. Shi, and Y. Xie. A Multi-Objective Genetic Algorithm Based on Quick Sort. Proceedings of 2004 Canadian Conference on Artificial Intelligence (AI'2004), 2004. To appear.

  43. J. Wang and C.X. Ling. Artificial Aging of Human faces Using Support Vector Machines. Proceedings of 2004 Canadian Conference on Artificial Intelligence (AI'2004), 2004. To appear.

  44. J. Zheng, C.X. Ling, Z. Shi, and Y. Xie. Some Discussions about MOGAs: Individual Relations, Non-dominated Set, and Application on Automatic Negotiation. Proceedings of the 2004 IEEE Congress on Evolutionary Computation, 2004. To appear.

  45. C.X. Ling, J. Huang, and H. Zhang. AUC: a Statistically Consistent and more Discriminating Measure than Accuracy. Proceedings of IJCAI 2003, 2003. (PS file) and (pdf file)

  46. C.X. Ling and J. Yan, Decision Tree with Better Ranking. Proceedings of 2003 International Conference on Machine Learning (ICML'2003), 2003. (PS file)

  47. J. Huang, J. Lu, and C.X. Ling, Comparing Naive Bayes, Decision Trees, and SVM using Accuracy and AUC. Proceedings of The Third IEEE International Conference on Data Mining (ICDM'2003), 2003. (PS file)

  48. Q. Yang, J. Yin, C.X. Ling, and T. Chen, Postprocessing Decision Trees to Extract Actionable Knowledge. Proceedings of The Third IEEE International Conference on Data Mining (ICDM'2003), 2003. (PS file)

  49. C.X. Ling, J. Huang, and H. Zhang. AUC: a Better Measure than Accuracy in Comparing Learning Algorithms. Proceedings of 2003 Canadian Artificial Intelligence Conference, 2003. (PS file)

  50. H. Zhang and C.X. Ling. A Fundamental Issue of Naive Bayes. Proceedings of 2003 Canadian Artificial Intelligence Conference, 2003. (PS file)

  51. C.X. Ling, T. Chen, Q. Yang and J. Chen. Mining Optimal Actions for Profitable CRM. Proceedings of The 2002 IEEE International Conference on Data Mining (ICDM'2002), 2002. (PDF file)

  52. H. Zhang and C.X. Ling. Representational upper bounds of Bayesian networks, to appear in Proceedings of the Nineteenth International Conference on Machine Learning (ICML), 2002. (PS file)

  53. C.X. Ling and H. Zhang. Toward Bayesian Classifiers with Accurate Probabilities. Proceedings of The Sixth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2002. (PDF file)

  54. H. Zhang and C.X. Ling. The Geometric Properties of Naive Bayes in Nominal Domains. Proceedings of 12th European Conference on Machine Learning (ECML). Springer, 2001. (PS file)

  55. H. Zhang and C.X. Ling. Learnability of Augmented Naive Bayes in the Nominal Domain. Proceedings of the Eighteenth International Conference on Machine Learning (ICML-2001). pages 617-623, Morgan Kaufmann. (PS file)

  56. H. Zhang and C.X. Ling. An Improved Learning Algorithm for Augmented Naive Bayes. The Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2001. (PS file)

  57. C.X. Ling, J. Gao, H. Zhang, W. Qian, and H. Zhang. Mining Generalized Query Patterns from Web Logs. Proceedings of the Thirty-Fourth Hawaii International Conference on System Sciences (HICSS-34). 2001. (PDF file)

  58. H. Zhang, C.X. Ling, and Z. Zhao. The Learnability of Naive Bayes. Proceedings of Canadian Artificial Intelligence Conference, pages 432-441, Springer, 2000. (PS file)

  59. C.X. Ling. Artificial Intelligence for Improving Children's Thinking. Proceedings of Second International Conference on Cognitive Science. 1999. (PS file) (PDF file)

  60. C.X. Ling and C. Li. Data Mining for Direct Marketing - Specific Problems and Solutions. Proceedings of Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), pages 73 - 79. 1998. (PDF file)

  61. C.X. Ling. Issues in Comparing Symbolic and Connectionist Models. Proceedings of the 13th Annual Conference of the Cognitive Science Society. 1998.

  62. T. Zhu, W. Gao, C.X. Ling, Z. Gao, and J. Li. Learning Mappings between Chinese Isolated Syllables and Syllables in Phrase with Backpropagation NeuralNets. Artificial Neural Networks in Engineering (ANNIE '98), St. Louis, Missouri November, 1998.

  63. C.X. Ling and H. Wang. Alignment Algorithms for Learning to Read Aloud. In Proceedings of IJCAI-97 (Fifteenth International Joint Conference on Artificial Intelligence), pages 874 - 879. 1997. (PS file)

  64. C.X. Ling. Symbolic models for cognitive learning. Proceedings of First International Conference on Cognitive Science. 1997.

  65. C.X. Ling and H. Wang. Learning Classifications from Multiple Sources of Unsupervised Data. Advances in Artificial Intelligence - Proceedings of the Eleventh Biennial Conference of the Canadian Society for Computational Studies of Intelligence. Pages 284 - 295. 1996.

  66. C.X. Ling. Can Symbolic Algorithms Model Cognitive Development? Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. pages 67 - 68. 1996. Invited Symposium paper.

  67. W.C. Schmidt and C.X. Ling. A Symbolic Model of Cognitive Transition: The Balance Scale Task. Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society. Pages 655 - 659. 1996.

  68. C.X. Ling. More on Overfitting in Learning Discrete Patterns. Proceedings of 1996 International Conference on Neural Information Processing. Pages 266 -270. 1996.

  69. D.W. Aha, S. Lapointe, C.X. Ling, and S. Matwin. Learning recursive relations with randomly selected small training sets. Proceedings of 11th International Conference on Machine Learning. pages 12 - 18. 1994. (PS file)

  70. C.X. Ling, Predicting irregular past tenses. Proceedings of The Sixteenth Annual Conference of the Cognitive Science Society, pages 577 - 582. 1994. (PS file)

  71. M.A. Gadalla, W.H. ElMaraghy, and C.X. Ling. Application of machine learning in manufacturing as applied to single machine loading. Proceedings of the CSME (Canadian Society for Mechanical Engineering) Forum 1994, pages 700 - 711. 1994.

  72. D.W. Aha, S. Lapointe, C.X. Ling, and S. Matwin. Inverting implication with small training sets. In Proceedings of the 1994 European Conference on Machine Learning. pages 31 - 48. Springer Verlag, 1994.

  73. C.X. Ling, S. Cherwenka, and M. Marinov. A symbolic model for learning the past tenses of English verbs. In Proceedings of IJCAI-93 (Thirteenth International Joint Conference on Artificial Intelligence), pages 1143 - 1149. Morgan Kaufmann Publishers, 1993.

  74. S. Lapointe, C.X. Ling, and S. Matwin. Constructive inductive logic programming. In Proceedings of IJCAI-93 (Thirteenth International Joint Conference on Artificial Intelligence), pages 1130 - 1136. Morgan Kaufmann Publishers, 1993.

  75. C.X. Ling and R. Buchal. Learning to control dynamic systems with automatic quantization. In Pavel B. Brazdil, editor, Proceedings of 1993 European Conference on Machine Learning. (Machine Learning: Lecture Notes in Artificial Intelligence 667), pages 372 - 377. Springer-Verlag, 1993.

  76. C.X. Ling. Inductive learning from good examples. In Proceedings of Twelfth International Joint Conference on Artificial Intelligence (IJCAI-91), pages 751 - 756. Morgan Kaufmann Publishers, 1991. (PS file)