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Technion - Computer Science Department - Ph.D. Thesis PHD-2008-12 - 2008 References Abe, N., Zadrozny, B., & Langford, J. (2004). An iterative method for multiclass cost-sensitive learning. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD- 2004), Seattle, WA, USA. Arnt, A., & Zilberstein, S. (2005). Learning policies for sequential time and cost sensitive classification. In Proceedings of the 1st international workshop on Utility-based data mining (UBDM’05) held with KDD’05, pp. 39–45, New York, NY, USA. ACM Press. Asuncion, A., & Newman, D. (2007). UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http://www.ics.uci.edu/∼mlearn/MLRepository.html. Auer, P., Holte, R., & Maass, W. (1995). Theory and applications of agnostic pac-learning with small decision trees. In Proceedings of the 12th International Conference on Machine Learning (ICML-1995), pp. 21–29. Morgan Kaufmann. Baram, Y., El-Yaniv, R., & Luz, K. (2003). Online choice of active learning algorithms. In Proceedings of the 20 International Conference on Machine Learning (ICML-2003), pp. 19–26, Washington, DC, USA. Bayer-Zubek, V., & Dietterich (2005). Integrating learning from examples into the search for diagnostic policies. Artificial Intelligence, 24, 263–303. Bennett, K. (1994). Global tree optimization: A non-greedy decision tree algorithm. Computing Science and Statistics, 26, 156–160. Berkman, N. C., & Sandholm, T. W. (1995). What should be minimized in a decision tree: A re-examination. Tech. rep. UM-CS-1995-020, Computer Science Department, University of Massachusetts at Amherst, Amherst, MA. Bilgic, M., & Getoor, L. (2007). Voila: Efficient feature-value acquisition for classification. In Proceedings of the 22nd National Conference on Artificial Intelligence (AAAI-2007). 156
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