Mitchell (eds.), Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, San Mateo, CA. Quinlan, J. R. (1986). Induction of decision trees, In Machine Learning 1(1):81-106.  Home/Search Document Not in Database Summary Related Articles Check |
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Wrappers For Performance Enhancement And Oblivious Decision Graphs - Kohavi (1995) (43 citations) (Correct)
....several hard tasks at one time. Because subgraphs may be shared, learning CHAPTER 6. OBLIVIOUS READ ONCE DECISION GRAPHS 185 may proceed faster, an idea that was exploited for Explanation Based Learning by Mitchell Thrun (1993) and more recently, through the learning of invariants, by Thrun Mitchell (1995). 6.7.2 Learning Decision Graphs Oliveira Sangiovanni Vincentelli (1995) described an algorithm for learning OODGs, under the name Reduced Ordered Decision Graphs. The algorithm starts from a decision tree and converts it to an OODG using the minimum description length principle (Rissanen 1986, ....
Mitchell, eds, "Machine learning: An Artificial Intelligence Approach", Vol. 1, Morgan Kaufmann Publishers, Inc. Singh, M. & Provan, G. M. (1995), A comparison of induction algorithms for selective and non-selective bayesian classifiers, in "Machine Learning: Proceedings of the Twelfth International Conference".
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....The use of such knowledge in order to guide the next evolution steps is discussed, and a hybrid algorithm interleaving evolution and induction is proposed. Section 3 presents an experimental study of several ML based controls of evolution. Besides two well studied GA problems, the Royal Road (Mitchell et al. 1993, Mitchell Holland 1993) and a GA deceptive problem (Whitley 1991) a combinatorial optimization problem is considered: the multiple knapsack problem (Khuri et al. 1994, Petersen 1967) The scope and limitations of ML based control are discussed in section 4, with respect to related work devoted ....
....classical and modern periods. In addition, this approach gives a unique opportunity to study the roles respectively devoted to mutation and crossover, by comparing what happens when mutations only, then crossovers only, are controlled. Three problems are considered: the Royal Road problem (Mitchell et al. 1993), a GA deceptive problem (Whitley 1991) and a combinatorial optimization problem (Khuri et al. 1994) 3.1 EXPERIMENTAL SETTINGS The evolutionary algorithm is a standard GA (Goldberg 1989) with bit string encoding, roulette wheel selection with fitness scaling, two points crossover at a rate ....
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Mitchell (ed.), Machine Learning : an artificial intelligence approach. Morgan Kaufmann. Mitchell, M., Forrest, S. & Holland, J.H. (1993). The royal road for genetic algorithms : Fitness landscapes and ga performance.
Andreas Ittner - Dept Of Computer (Correct)
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Mitchell (eds.), Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, San Mateo, CA. Quinlan, J. R. (1986). Induction of decision trees, In Machine Learning 1(1):81-106.
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. Knowledge of domain effects in problem representation: The case of Tower of Hanoi isomorphs. Thinking and Reasoning,
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Mitchell, (Eds.), Machine Learning: An Artificial Intelligence Approach, volume 2, pages 45--62. Morgan Kaufmann. Wittgenstein, L. (1953). Philosophical Investigations. New York: MacMillan.
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Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach. Los Altos: Morgan Kaufmann. Iba, G. (1979). Learning disjunctive concepts from examples. Massachusetts Institute of Technology A.I. Memo 548, Cambridge, MA.
Using Decision Trees to Improve Case-Based Learning - Cardie (1993) (45 citations) (Correct)
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Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach. Palo Alto: Morgan Kaufmann. Resnik, P. (1992). A class-based approach to lexical discovery. Proceedings, 30th Annual Meeting of the Association for Computational Linguistics, pp. 327329.
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Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach, pp. 463--482. Palo Alto: Tioga. Riesbeck, C. K. and R. C. Schank (1989). Inside Case-Based Reasoning. Hillsdale, NJ: Lawrence Erlbaum Associates.
Learning Sequential Decision Rules Using Simulation Models and .. - Grefenstette (1990) (55 citations) (Correct)
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M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 2). Los Altos, CA: Morgan Kaufmann. Koza, J. R. (1989). Hierarchical genetic algorithms operating on populations of computer programs. Proceedings of the Eleventh International Joint Conference on Arti ficial Intelligence (pp. 768-774). Detroit: Morgan Kaufmann.
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Mitchell (eds.), Machine learning: An artificial intelligence approach, Vol. 2 (Los Altos, CA: Morgan Kaufmann). Holyoak, K. J. and Thagard, P. (1989). Analogical mapping by constraint satisfaction. Cognitive Science, 13: 295-355.
Efficient Learning and Planning Within the Dyna Framework - Peng, Williams (1993) (45 citations) (Correct)
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M. Mitchell (Eds.) Machine Learning: An Artificial Intelligence Approach, Volume II. Los Altos, CA: Morgan Kaufmann. Moore, A. W. (1991). Variable resolution dynamic programming: Efficiently learning action maps in multivariate real-valued state-spaces. Proceedings of the 8th International Machine Learning Workshop.
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Mitchell (Eds.): Machine learning: An artificial intelligence approach. Los Altos, CA: Morgan Kaufmann. Rescorla, R. A. (1968): Probability of shock in the presence and absence of CS in fear conditioning. Journal of Comparative and Physiological Psychology, 66, 1-5.
Using Genetic Algorithms for Supervised Concept Learning - Spears, De Jong (6 citations) (Correct)
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Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach (Vol. 2). Morgan Kaufmann Publishers, Los Altos, CA. Utgoff, Paul E. (1989). Improved Training via Incremental Learning, Proc. of the 6th Int'l Workshop on Machine Learning, 62-65.
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