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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
Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008 Sheng, V. S., & Ling, C. X. (2007a). Partial example acquisition in cost-sensitive learning. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD-2007), pp. 638–646, San Jose, California, USA. Sheng, V. S., & Ling, C. X. (2007b). Roulette sampling for cost-sensitive learning. In Proceedings of the 18th European Conference on Machine Learning (ECML-2007), pp. 724–731, Warsaw, Poland. Tan, M., & Schlimmer, J. C. (1989). Cost-sensitive concept learning of sensor use in approach and recognition. In Proceedings of the 6th International Workshop on Machine Learning, pp. 392–395, Ithaca, NY. Turney, P. (2000). Types of cost in inductive concept learning. In Proceedings of the Workshop on Cost-Sensitive Learning held with the 17th International Conference on Machine Learning (ICML-2000), Stanford, CA. Turney, P. D. (1995). Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm. Journal of Artificial Intelligence Research, 2, 369–409. Ueno, K., Xi, X., Keogh, E., & Lee, D. (2006). Anytime classification using the nearest neighbor algorithm with applications to stream mining. In Proceedings of the 6th IEEE International Conference on Data Mining (ICDM- 2006), pp. 623–632, Washington, DC, USA. Utgoff, P. E. (1989). Incremental induction of decision trees. Machine Learning, 4(2), 161–186. Utgoff, P. E., Berkman, N. C., & Clouse, J. A. (1997). Decision tree induction based on efficient tree restructuring. Machine Learning, 29(1), 5–44. Vadera, S. (2005). Inducing cost-sensitive non-linear decision trees. Technical report 03-05-2005, School of Computing, Science and Engineering, University of Salford. Vapnik, M. A. (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag. Wallace, R. J., & Freuder, E. C. (1995). Anytime algorithms for constraint satisfaction and SAT problems. In Working Notes of IJCAI-95 Workshop on Anytime Algorithms and Deliberation Scheduling. Webb, G. I. (1996). Further experimental evidence against the utility of Occam’s Razor. Journal of Artificial Intelligence Research, 4, 397–417. Witten, I. H., & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, CA. 165
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