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Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008 Quinlan, J. R., & Rivest, R. L. (1989). Inferring decision trees using the minimum description length principle. Information and Computation, 80(3), 227–248. R Development Core Team (2005). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Ragavan, H., & Rendell, L. (1993). Lookahead feature construction for learning hard concepts. In Proceedings of the 10th International Conference on Machine Learning (ICML-1993), pp. 252–259, Amherst, MA, USA. Rao, R., Gordon, D., & Spears, W. (1995). For every generalization action, is there really an equal or opposite reaction?. In Proceedings of 12th International Conference on Machine Learning (ICML-1995), pp. 471–479. Ray, S., & Page, D. (2005). Generalized skewing for functions with continuous and nominal attributes. In Proceedings of the 22nd International Conference on Machine Learning (ICML-2005), Bonn, Germany. Russell, S. J., & Wefald, E. (1989). Principles of metareasoning. In Proceedings of the 1st International Conference on Pronciples of Knowledge Representation and Reasoning, pp. 400–411, San Mateo, California. Russell, S. J., & Zilberstein, S. (1996). Optimal composition of real-time systems. Artificial Intelligence, 82(1-2), 181–213. Sarkar, U. K., Chakrabarti, P., Ghose, S., & DeSarkar, S. C. (1994). Improving greedy algorithms by look-ahead search. Journal of Algorithms, 16, 1–23. Schaerf, A. (1996). Tabu search techniques for large high-school timetabling problems. In Proceedings of the 13th National Conference on Artificial Intelligence (AAAI-1996), pp. 363–368, Portland, OR, UAS. Schaffer, C. (1994). A conservation law for generalization performance. In Proceedings of 11th International Conference on Machine Learning (ICML- 1994), pp. 259–265. Schapire, R. (1999). A brief introduction to boosting. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-1999), pp. 1401–1406, Stockholm, Sweden. Shavlik, J. W., Mooney, R. J., & G., G. T. (1991). Symbolic and neural learning algorithm: An experimental comparison. Machine Learning, 6, 111–143. Sheng, S., Ling, C. X., Ni, A., & Zhang, S. (2006). Cost-sensitive test strategies. In Proceedings of the 21st National Conference on Artificial Intelligence (AAAI-2006), Boston, MA, USA. Sheng, S., Ling, C. X., & Yang, Q. (2005). Simple test strategies for cost-sensitive decision trees. In Proceedings of the 9th European Conference on Machine Learning (ECML-2005), pp. 365–376, Porto, Portugal. 164
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