Reading list for the Intelligent Systems Program comprehensive examination

1. Machine Learning

1.1. General

• Mitchell. Machine Learning. 1996.

• Quinlan. Induction of decision trees. Machine Learning 1: pages 80-106, 1986.

• Cohen. Fast effective rule induction. Machine Learning, 1995.

• Furnkranz. Fossil: a robust relational learner. Machine Learning: ECML94, pages 122-137, 1994.

• Clark and Niblett. The CN2 Induction Algorithm. Machine Learning 3, 1989: pages 261-284.

• Provost, Aronis, Buchanan. Rule-space search for knowledge-based discovery. Report IS99-012, Information Systems Department, Stern School, New York University.

• Salmon. On vindicating induction. Philosophy of Science 30:3, pp pages 252-261, 1963.

• Kern-Isberner. Solving the inverse representation problem. 2000.

• Kern-Isberner. Discovering most informative association rules from data. 2001.

• Cohen. Learning trees and rules with set-valued features. AAAI 1: pages 709-716, 1996.

• Agrawal and Srikant. Fast algorithms for mining association rules. International Conference on Very Large Databases, pages 487-499. 1994.

• Schapire and Freund. Boosting the Margin: A new explanation for the effectiveness of voting methods. International Conference on Machine Learning 322-330, 1997.

• Schapire. The Boosting Approach to Machine Learning: An Overview. MSRI Workshop on Nonlinear Estimation and Classification, 2001.

1.2. Redundancy and Pruning

• Furnkranz. Pruning algorithms for rule learning. Machine Learning 27, 139-171, 1997.

• Agrawal, Mannila, Srikant, Toivonen, Verkamo. Fast discovery of association rules

• Shah, Lakshmanan, Ramamritham, Sudarchan. Interestingness and pruning of mined patterns. 1999 Workshop on Research Issues in Data Mining and Knowledge Discovery, 1999.

• Cristofor & Samovici. Generating informative cover for association rules., 2002.

• Zaki. Generating non-redundant association rules. Knowledge Discovery and Data Mining, pages 34-43, 2002.

• Pasquier, Taouil, Lakhal. Closed set based discovery of small covers for association rules. Proc. 15emes Journees Bases de Donnees Avancees, {BDA}, page 361-381, 1999.

• Liu, Hsu, Ma. Pruning and summarizing the discovered associations. Knowledge Discovery and Data Mining, pages 125-134, 1999.

• Toivonen, Klemetinen, Ronkainen, Haetoenen, Mannila. Pruning and grouping discovered association rules. MLnet Workshop on Statistics, Machine Learning and Discovery in Databases, pages 47-52, 1995.

• Bayardo. Brute-force mining of high-confidence classification rules. 3rd Internaitonal Conference on Knowledge Discovery and Data Mining, pages 123-126, 1997.

1.3. Discretization

• Doherty, Kohavi, Sahami. Supervized and unsupervized discretization of continuous features. 12th International Conference on Machine Learning, 1995.

• Kerber. ChiMerge: discretization of numeric attributes. 9th International Conference on Artificial Intelligence, 1992, pages 123-128.

• Kohavi and Sahami. Error-based and entropy-based discretization of continuous features, 1996.

• Wang and Liu. Concurrent discretization of continuous attributes. Pacific Rim International Conference on Artificial Intelligence, 1998.

Discovery and Theories

• Buchanan. Steps toward mechanizing discovery. Logic of Discovery and Diagnosis in Medicine, pages 94-114, 1985.

• Jones. Generating predictions to aid the scientific discovery process. Proceedings of the Fifth National Conference of the American Association for Artificial Intelligence, 513-517, 1986.

• Eliasmith and Thagard. Waves, particles, and explanatory coherence. British Journal for the Philosophy of Science 48: pages 1-19, 1997.

• Thagard and Verbeurgt. Coherence as constraint satisfaction. Cognitive Science, 22: 1-24, 1998.

• Thagard. Probabilistic networks and explanatory coherence. Cognitive Science Quarterly, 1: pages 91-114, 2000.

• Byrne. The converence of explanatory coherence and the story model: a case study of juror decision. Journal of Personality and Social Psychology 51, pages 242-258, 1994. (17p)

• Oates and Lenson. Large datasets lead to overly complex models: an explanation and a solution. Knowledge Discovery and Data Mining pages 294-298, 1998.

• Ourston and Mooney. Theory refinement combining analytical and empirical methods. Artificial Intelligence 66: pages 273-309, 1994.

• Hernandes-Orallo. Computational consilience as a basis for theory formation, 1998.

• Nock. Inducing intelligible classifiers without trading accuracy for simplicity: theoretical results, approximation algorithms and experiments, 2002.

• Pazzani, Mani and Shankle. Beyond concise and colorful: learning intelligible rules. Knowledge Discovery in Data Mining, pages 235-238, 1997.

• Cooper and Herskowitz. A bayesian method for the induction of probabilistic networks from data. Machine Learning:1 pages 309-347, 1992.

• Horty. Precedent, deontic logic and inheritance. International Conference on AI and Law, 1999, pages 63-72.

• Bruninghaus and Ashley. Predicting outcomes of case-based legal arguments. International Conference on AI and Law, 2003, pages 233-242.

• Ashley and Bruninghaus. Predictive role for intermediate legal concepts. Jurix Conference, 2003 (to appear).

Uncertain Reasoning

• Russel and Norvig. Artificital Intelligence, chapters 13-17 (pages 462-646), 2003.

• Jordan. Introduction to probabilistic graphical models. Chapters 1-5. To appear.

• Heckerman. Tutorial on learning with bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, 1995. (58 pages)

• Duda, Hart, Stork. Pattern Classification (2nd edition), chapters 1-3, 2000.

• Pearl. Causality: Models Reasoning and Inference. Chapters 1-4 (pages 1-126), 2000.

• Mitchell. Machine Learning, Chapter 6 (pages 154-199), 1996.

• Druzdzel and Simon. Causality in bayesian belief networks. Conference on Incertainty in Artificial Intelligence, pages 3-11, 1993.

• Druzdzel. Some Properties of Joint Probability Distributions. Conference on Uncertainty in Artificial Intelligence, pages 187-194, 1994.

• Herskovits & Cooper. Kutato: an entropy-driven system for construction of probabilistic expert systems from data. Technical report KSL-90-22, Knowledge Systems Lab, 1990.

• Henrion, Breeze and Horvitz. Decision analysis and expert systems. AI Magazine 12, pages 64-91, 1991.

• Sonnenberg and Beck. Markov models in medical decision making - a practical guide. Medical Decision Making, pages 322-338, 1993.

• Goodman. Toward evidence-based medical statistics: the p-value fallacy. Annals of Internal Medicine, pages 996-1004, 1999.

• Goodman. Toward evidence-based medical statistics: the bayes factor. Annals of Internal Medicine, pages 1005-1013, 1999.

• Franks and Burke. Standing statistics the right side up. Annals of Internal Medicine, pages 1019-1021, 1999.