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Publications of Shaul Markovitch

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  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. Ofer Egozi, Evgeniy Gabrilovich and Shaul Markovitch. Concept-Based Feature Generation and Selection for Information Retrieval. In Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, Chicago, IL, 2008. [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. Evgeniy Gabrilovich and Shaul Markovitch. Harnessing the Expertise of 70,000 Human Editors: Knowledge-Based Feature Generation for Text Categorization. Journal of Machine Learning Research, 8:2297-2345, 2007. [pdf][abstract]
  5. Saher Esmeir and Shaul Markovitch. Anytime Learning of Decision Trees. Journal of Machine Learning Research, 8:891-933, 2007. [pdf][abstract]
  6. Saher Esmeir and Shaul Markovitch. Occam's Razor Just Got Sharper. In Proceedings of The Twentieth International Joint Conference for Artificial Intelligence, pages 768-773, Hyderabad, India, 2007. [pdf][abstract]
  7. Evgeniy Gabrilovich and Shaul Markovitch. Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis. In Proceedings of The Twentieth International Joint Conference for Artificial Intelligence, pages 1606-1611, Hyderabad, India, 2007. [pdf][abstract]
  8. Saher Esmeir and Shaul Markovitch. When a Decision Tree Learner Has Plenty of Time. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pages 1597-1600, Boston, MA, 2006. [pdf][abstract]
  9. Saher Esmeir and Shaul Markovitch. Anytime Induction of Decision Trees: an Iterative Improvement Approach. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pages 348-355, Boston, MA, 2006. [pdf][abstract]
  10. Nela Gurevich, Shaul Markovitch and Ehud Rivlin. Active Learning with Near Misses. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pages 362-367, Boston, MA, 2006. [pdf][abstract]
  11. Evgeniy Gabrilovich and Shaul Markovitch. Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, pages 1301-1306, Boston, MA, 2006. [pdf][abstract]
  12. Dmitry Davidov and Shaul Markovitch. Multiple-goal Heuristic Search. Journal of Artificial Intelligence Research, 26:417-451, 2006. [pdf][abstract]
  13. Asaf Amit and Shaul Markovitch. Learning to Bid in Bridge. Machine Learning, 63:287-327, 2006. [pdf][abstract]
  14. Shaul Markovitch and Oren Shnitzer. Self-consistent Batch-Classification. Technical report CIS-2005-04, Technion, 2005. [pdf][abstract]
  15. 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]
  16. Yaniv Hamo and Shaul Markovitch. The Compset Algorithm for Subset Selection. In Proceedings of The Nineteenth International Joint Conference for Artificial Intelligence, pages 728-733, Edinburgh, Scotland, 2005. [pdf][abstract]
  17. Evgeniy Gabrilovich and Shaul Markovitch. Feature Generation for Text Categorization Using World Knowledge. In Proceedings of The Nineteenth International Joint Conference for Artificial Intelligence, pages 1048-1053, Edinburgh, Scotland, 2005. [pdf][abstract]
  18. Shaul Markovitch and Ronit Reger. Learning and Exploiting Relative Weaknesses of Opponent Agents. Autonomous Agents and Multi-agent Systems, 10:103-130, 2005. [pdf][abstract]
  19. Evgeniy Gabrilovich and Shaul Markovitch. Text Categorization with Many Redundant Features: Using Aggressive Feature Selection to Make SVMs Competitive with C4.5. In Proceedings of The Twenty-First International Conference on Machine Learning, pages 321-328, Banff, Alberta, Canada, 2004. Morgan Kaufmann. [pdf][abstract]
  20. Dmitry Davidov, Evgeniy Gabrilovich and Shaul Markovitch. Parameterized Generation of Labeled Datasets for Text Categorization Based on a Hierarchical Directory. In Proceedings of The 27th Annual International ACM SIGIR Conference, pages 250-257, Sheffield, UK, 2004. ACM Press. [pdf][abstract]
  21. 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]
  22. Michael Lindenbaum, Shaul Markovitch and Dmitry Rusakov. Selective Sampling for Nearest Neighbor Classifiers. Machine Learning, 54:125-152, 2004. [pdf][abstract]
  23. Lev Finkelstein, Shaul Markovitch and Ehud Rivlin. Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources. Journal of Artificial Intelligence Research, 19:73-138, 2003. [pdf][abstract]
  24. Shaul Markovitch and Asaf Shatil. Speedup Learning for Repair-based Search by Identifying Redundant Steps. Journal of Machine Learning Research, 4:649-682, 2003. [pdf][abstract]
  25. Orna Grumberg, Shlomi Livne and Shaul Markovitch. Learning to Order {BDD} Variables in Verification. Journal of Artificial Intelligence Research, 2003. [pdf][abstract]
  26. Lev Finkelstein, Shaul Markovitch and Ehud Rivlin. Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Independent Processes. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, pages 719-724, Edmonton, Alberta, Canada, 2002. [pdf][abstract]
  27. Dmitry Davidov and Shaul Markovitch. Multiple-goal Search Algorithms and their Application to Web Crawling. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, pages 713-718, Edmonton, Alberta, Canada, 2002. [pdf][abstract]
  28. Shaul Markovitch and Danny Rosenstein. Feature Generation Using General Constructor Functions. Machine Learning, 49:59-98, 2002. [pdf][abstract]
  29. Lev Finkelstein, Shaul Markovitch and Ehud Rivlin. Optimal Schedules for Parallelizing Anytime Algorithms. In Proceedings of The AAAI Fall Symposium on Using Uncertainty within Computation, pages 49-56, North Carolina, 2001. [pdf][abstract]
  30. Lev Finkelstein and Shaul Markovitch. Optimal schedules for monitoring anytime algorithms. Artificial Intelligence, 2001. [pdf][abstract]
  31. Shaul Markovitch. Applications of Macro Learning to Path Planning. Technical report CIS9907, Technion, 1999. [pdf][abstract]
  32. 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]
  33. 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]
  34. Oleg Ledeniov and Shaul Markovitch. The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference. Journal of Artificial Intelligence Research, 9:37-97, 1998. [pdf][abstract]
  35. Lev Finkelstein and Shaul Markovitch. A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle. Journal of Artificial Intelligence Research, 8:223-263, 1998. [pdf][abstract]
  36. David Carmel and Shaul Markovitch. Model-based Learning of Interaction Strategies in Multi-Agent Systems. Journal of Experimental and Theoretical Artificial Intelligence, 10:309-332, 1998. [pdf][abstract]
  37. David Carmel and Shaul Markovitch. Pruning Algorithms for Multi-Model Adversary Search. Artificial Intelligence, 99:325-355, 1998. [pdf][abstract]
  38. Lev Finkelstein and Shaul Markovitch. Learning to Play Chess Selectively by Acquiring Move Patterns. ICCA Journal, 21:100-119, 1998. [pdf][abstract]
  39. Oleg Ledeniov and Shaul Markovitch. Learning Investment Functions for Controlling the Utility of Control Knowledge. In Proceedings of the Fifteenth National Conference on Artificial Intelligence, pages 463-468, Madison, Wisconsin, 1998. [pdf][abstract]
  40. 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]
  41. Oleg Ledeniov and Shaul Markovitch. Controlled Utilization of Control Knowledge for Speeding up Logic Inference. Technical Report CIS9812, Technion, 1998. [pdf][abstract]
  42. 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]
  43. David Carmel and Shaul Markovitch. Incorporating Opponent Models into Adversary Search. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 120-125, Portland, Oregon, 1996. [pdf][abstract]
  44. David Carmel and Shaul Markovitch. Learning Models of Intelligent Agents. In Proceedings of the Thirteenth National Conference on Artificial Intelligence, pages 62-67, Portland, Oregon, 1996. [pdf][abstract]
  45. David Carmel and Shaul Markovitch. Learning and Using Opponent Models in Adversary Search. Technical Report CIS9609, Technion, 1996. [pdf][abstract]
  46. David Carmel and Shaul Markovitch. Opponent Modeling in Multi-agent Systems. In Gerhard Weiss and Sandip Sen, editors, Adaption And Learning In Multi-Agent Systems, volume 1042 of Lecture Notes in Artificial Intelligence. Springer-Verlag, 1996. [pdf][abstract]
  47. Shaul Markovitch and Yaron Sella. Learning of Resource Allocation Strategies for Game Playing. Computational Intelligence, 1996. [pdf][abstract]
  48. Uri Keidar, Shaul Markovitch and Erez Webman. Utilization Filtering of Macros Based on Goal Similarity. Technical Report CIS9608, Technion, 1996. [pdf][abstract]
  49. Ido Dagan, Shaul Marcus and Shaul Markovitch. Contextual Word Similarity and Estimation from Sparse Data. Computer Speech and Language, 9:123-152, 1995. [pdf][abstract]
  50. David Carmel and Shaul Markovitch. The M* Algorithm: Incorporating Opponent Models Into Adversary Search. Technical Report CIS9402, Computer Science Department, Technion, 1994. [pdf][abstract]
  51. Shaul Markovitch and Paul D. Scott. Information Filtering: Selection Mechanisms in Learning Systems. Machine Learning, 10:113-151, 1993. [pdf][abstract]
  52. Shaul Markovitch and Yaron Sella. Learning of Resource Allocation Strategies for game Playing. In Proceedings of The Thirteenth International Joint Conference for Artificial Intelligence, pages 974-979, Chambery, France, 1993. [pdf][abstract]
  53. David Carmel and Shaul Markovitch. Learning Models of the Opponent's Strategy in Game Playing. In Proceedings of The AAAI Fall Symposium on Games: Planing and Learning, pages 140-147, North Carolina, 1993. [pdf][abstract]
  54. David Lorenz and Shaul Markovitch. Derivative Evaluation Function Learning Using Genetic Operators. In Proceedings of The AAAI Fall Symposium on Games: Planing and Learning, pages 106-114, New Carolina, 1993. [pdf][abstract]
  55. Ido Dagan, Shaul Marcus and Shaul Markovitch. Contextual Word Similarity and Estimation from Sparse Data. In Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pages 164-171, Ohio State University, 1993. [pdf][abstract]
  56. Paul D. Scott and Shaul Markovitch. Experience Selection and Problem Choice in an Exploratory Learning System. Machine Learning, 12:49-67, 1993. [pdf][abstract]
  57. Shaul Markovitch and Irit Rosdeutscher. Systematic Experimentation with Deductive Learning: Satisficing vs. Optimizing Search. In Proceedings of the Knowledge Compilation and Speedup Learning Workshop, Aberdeen, Scotland, 1992. [pdf][abstract]
  58. Paul D. Scott and Shaul Markovitch. Representation Generation in An Exploratory Learning System. In D. Fisher and M. Pazzani, editors, Concept Formation: Knowledge and Experience in Unsupervised Learning. Morgan Kaufmann, 1991. [pdf][abstract]
  59. Reuven A. Hasson, Shaul Markovitch and Yaron Sella. Using Filters to Improve Efficiency of Game-playing Learning Procedures. In Proceedings of Eleventh International Conference of the Chilean Computer Science Society, pages 125-137, Santiago, Chile, 1991. [pdf][abstract]
  60. Paul D. Scott and Shaul Markovitch. Knowledge Considered Harmful. In Proceedings of IEEE Colloquium on Knowledge Engineering, London, 1990. [pdf][abstract]
  61. Marcial Losada and Shaul Markovitch. GroupAnalyzer: {A} System for Dynamic Analysis of Group Interaction. In Proceedings of 23rd Hawaii International Conference for System Sciences, pages 101-110, Kailua-Kona,Hawaii, 1990. [pdf][abstract]
  62. Shaul Markovitch and Paul D. Scott. Utilization Filtering: a Method for Reducing the Inherent Harmfulness of Deductively Learned Knowledge. In Proceedings of The Eleventh International Joint Conference for Artificial Intelligence, pages 738-743, Detroit, Michigan, 1989. [pdf][abstract]
  63. Shaul Markovitch and Paul D. Scott. Information Filters and Their Implementation in the {SYLLOG} System. In Proceedings of The Sixth International Workshop on Machine Learning, pages 404-407, Ithaca, New York, 1989. Morgan Kaufmann. [pdf][abstract]
  64. Shaul Markovitch. Information Filtering: Selection Mechanisms in Learning Systems. PhD Thesis, EECS Department, University of Michigan, 1989 [pdf][abstract]
  65. Shaul Markovitch and Paul D. Scott. Automatic Ordering of Subgoals --- A Machine Learning Approach. In Proceedings of the North American Conference on Logic Programming, pages 224-242, Cleveland, Ohio, USA, 1989. [pdf][abstract]
  66. Paul D. Scott and Shaul Markovitch. Uncertainty Based Selection of Learning Experiences. In Proceedings of The Sixth International Workshop on Machine Learning, pages 358-361, Ithaca, New York, 1989. Morgan Kaufmann. [pdf][abstract]
  67. Paul D. Scott and Shaul Markovitch. Learning Novel Domains Through Curiosity and Conjecture. In Proceedings of International Joint Conference for Artificial Intelligence, pages 669-674, Detroit, Michigan, 1989. [pdf][abstract]
  68. McLeod, Poppy L, Liker, Jeffrey K. Lobel, Sharon A. Spreitzer, Gretchen Marie., Losada, Marcial F and Markovitch, Shaul. Process feedback in task-oriented small groups. Working Paper 602, University of Michigan. School of Business Administration. Division of Research, 1989. [pdf][abstract]
  69. Shaul Markovitch and Paul D. Scott. The Role of Forgetting in Learning. In Proceedings of The Fifth International Conference on Machine Learning, pages 459-465, Ann Arbor, MI, 1988. Morgan Kaufmann. [pdf][abstract]
  70. Shaul Markovitch and Paul D. Scott. Knowledge Considered Harmful. Technical Report 030788, The Center for Machine Intelligence, Ann Arbor, MI, 1988. [pdf][abstract]