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Publications Related to: Lookahead

By Shaul Markovitch

  1. Saher Esmeir and Shaul Markovitch. Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach. Journal of Artificial Intelligence Research, 33:1-31, 2008. [pdf][abstract]
  2. Saher Esmeir and Shaul Markovitch. Anytime Learning of Decision Trees. Journal of Machine Learning Research, 8:891-933, 2007. [pdf][abstract]
  3. Saher Esmeir and Shaul Markovitch. Anytime Induction of Cost-sensitive Trees. In Proceedings of The 21st Conference on Neural Information Processing Systems (NIPS-2007), , 2007. [pdf][abstract]
  4. Saher Esmeir and Shaul Markovitch. Interruptible Anytime Algorithms for Iterative Improvement of Decision Trees. In Proceedings of the 1st international workshop on Utility-based data mining, pages 78-85, Chicago, Illinois, 2005. [pdf][abstract]
  5. Saher Esmeir and Shaul Markovitch. Lookahead-based Algorithms for Anytime Induction of Decision Trees. In Proceedings of The Twenty-First International Conference on Machine Learning, pages 257-264, Banff, Alberta, Canada, 2004. Morgan Kaufmann. [pdf][abstract]
  6. Michael Lindenbaum, Shaul Markovitch and Dmitry Rusakov. Selective Sampling for Nearest Neighbor Classifiers. Machine Learning, 54:125-152, 2004. [pdf][abstract]
  7. David Carmel and Shaul Markovitch. Exploration Strategies for Model-based Learning in Multiagent Systems. Autonomous Agents and Multi-agent Systems, 2:141-172, 1999. [pdf][abstract]
  8. Michael Lindenbaum, Shaul Markovitch and Dmitry Rusakov. Selective Sampling for Nearest Neighbor Classifiers. In The Proceedings of the Sixteenth National Confernce on Artificial Intelligence, pages 366-371, Orlando, Florida, 1999. [pdf][abstract]
  9. David Carmel and Shaul Markovitch. How to explore your opponent's strategy (almost) optimally. In Proceedings of the Third International Conference on Multi-Agent Systems, pages 64-71, Paris, France, 1998. [pdf][abstract]
  10. David Carmel and Shaul Markovitch. Exploration and Adaptation in Multiagent Systems: A Model-Based Approach. In Proceedings of The Fifteenth International Joint Conference for Artificial Intelligence, pages 606-611, Nagoya, Japan, 1997. [pdf][abstract]