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Note: Links lead to the DBLP on the Web. Shaul Markovitch Shaul Markovitch, Ronit Reger : Learning and Exploiting Relative Weaknesses of Opponent Agents. Autonomous Agents and Multi-Agent Systems 10 (2): 103-130 (2005) Saher Esmeir , Shaul Markovitch: Lookahead-based algorithms for anytime induction of decision trees. ICML 2004 Evgeniy Gabrilovich , Shaul Markovitch: Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5. ICML 2004 Dmitry Davidov , Evgeniy Gabrilovich , Shaul Markovitch: Parameterized generation of labeled datasets for text categorization based on a hierarchical directory. SIGIR 2004 : 250-257 Michael Lindenbaum , Shaul Markovitch, Dmitry Rusakov : Selective Sampling for Nearest Neighbor Classifiers. Machine Learning 54 (2): 125-152 (2004) Orna Grumberg , Shlomi Livne , Shaul Markovitch: Learning to Order BDD Variables in Verification. J. Artif. Intell. Res. (JAIR) 18 : 83-116 (2003) Lev Finkelstein , Shaul Markovitch, Ehud Rivlin : Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Shared Resources. J. Artif. Intell. Res. (JAIR) 19 : 73-138 (2003) Shaul Markovitch, Asaf Shatil : Speedup Learning for Repair-based Search by Identifying Redundant Steps. Journal of Machine Learning Research 4 : 649-682 (2003) Dmitry Davidov , Shaul Markovitch: Multiple-Goal Search Algorithms and their Application to Web Crawling. AAAI/IAAI 2002 : 713-718 Lev Finkelstein , Shaul Markovitch, Ehud Rivlin : Optimal Schedules for Parallelizing Anytime Algorithms: The Case of Independent Processes. AAAI/IAAI 2002 : 719-724 Shaul Markovitch, Dan Rosenstein : Feature Generation Using General Constructor Functions. Machine Learning 49 (1): 59-98 (2002) Lev Finkelstein , Shaul Markovitch: Optimal schedules for monitoring anytime algorithms. Artif. Intell. 126 (1-2): 63-108 (2001) Michael Lindenbaum , Shaul Markovitch, Dmitry Rusakov : Selective Sampling for Nearest Neighbor Classifiers. AAAI/IAAI 1999 : 366-371 David Carmel , Shaul Markovitch: Exploration Strategies for Model-based Learning in Multi-agent Systems: Exploration Strategies. Autonomous Agents and Multi-Agent Systems 2 (2): 141-172 (1999) Oleg Ledeniov , Shaul Markovitch: Learning Investment Functions for Controlling the Utility of Control Knowledge. AAAI/IAAI 1998 : 463-468 David Carmel , Shaul Markovitch: How to Explore your Opponent's Strategy (almost) Optimally. ICMAS 1998 : 64-71 David Carmel , Shaul Markovitch: Pruning Algorithms for Multi-Model Adversary Search. Artif. Intell. 99 (2): 325-355 (1998) Lev Finkelstein , Shaul Markovitch: A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle CoRR cs.AI/9806102 : (1998) Lev Finkelstein , Shaul Markovitch: A Selective Macro-learning Algorithm and its Application to the NxN Sliding-Tile Puzzle. J. Artif. Intell. Res. (JAIR) 8 : 223-263 (1998) Oleg Ledeniov , Shaul Markovitch: The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic Inference. J. Artif. Intell. Res. (JAIR) 9 : 37-97 (1998) David Carmel , Shaul Markovitch: Model-based learning of interaction strategies in multi-agent systems. J. Exp. Theor. Artif. Intell. 10 (3): 309-332 (1998) David Carmel , Shaul Markovitch: Exploration and Adaptation in Multiagent Systems: A Model-based Approach. IJCAI (1) 1997 : 606-611 David Carmel , Shaul Markovitch: Incorporating Opponent Models into Adversary Search. AAAI/IAAI, Vol. 1 1996 : 120-125 David Carmel , Shaul Markovitch: Learning Models of Intelligent Agents. AAAI/IAAI, Vol. 1 1996 : 62-67 Shaul Markovitch, Yaron Sella : Learning of Resource Allocation Strategies for Game Playing. Computational Intelligence 12 : 88-105 (1996) David Carmel , Shaul Markovitch: Opponent Modeling in Multi-Agent Systems. Adaption and Learning in Multi-Agent Systems 1995 : 40-52 Ido Dagan , Shaul Marcus , Shaul Markovitch: Contextual Word Similarity and Estimation from Sparse Data. ACL 1993 : 164-171 Shaul Markovitch, Yaron Sella : Learning of Resource Allocation Strategies for Game Playing. IJCAI 1993 : 974-979 Shaul Markovitch, Paul D. Scott : Information Filtering: Selection Mechanisms in Learning Systems. Machine Learning 10 : 113-151 (1993) Paul D. Scott , Shaul Markovitch: Experience Selection and Problem Choice in an Exploratory Learning System. Machine Learning 12 : 49-67 (1993) Paul D. Scott , Shaul Markovitch: Learning Novel Domains Through Curiosity and Conjecture. IJCAI 1989 : 669-674 Shaul Markovitch, Paul D. Scott : Utilization Filtering: A Method for Reducing the Inherent Harmfulness of Deductively Learned Knowledge. IJCAI 1989 : 738-743 Paul D. Scott , Shaul Markovitch: Uncertainty Based Selection of Learning Experiences. ML 1989 : 358-361 Shaul Markovitch, Paul D. Scott : Information Filters and Their Implementation in the SYLLOG System. ML 1989 : 404-407 Shaul Markovitch, Paul D. Scott : Automatic Ordering of Subgoals - A Machine Learning Approach. NACLP 1989 : 224-240 Shaul Markovitch, Paul D. Scott : The Role of Forgetting in Learning. ML 1988 : 459-465 1 [ 11 ] [ 13 ] [ 14 ] [ 15 ] [ 16 ] [ 20 ] [ 21 ] [ 23 ] 2 [ 10 ] 3 [ 28 ] [ 33 ] 4 [ 35 ] 5 [ 18 ] [ 19 ] [ 25 ] [ 27 ] [ 30 ] 6 [ 33 ] [ 34 ] 7 [ 31 ] 8 [ 17 ] [ 22 ] 9 [ 24 ] [ 32 ] 10 [ 31 ] 11 [ 10 ] 12 [ 36 ] 13 [ 27 ] [ 30 ] 14 [ 26 ] 15 [ 24 ] [ 32 ] 16 [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ] [ 7 ] [ 8 ] 17 [ 9 ] [ 12 ] 18 [ 29 ]  ©2005 Association for Computing Machinery |
