T. M. Mitchell, J. G. Carbonell, and R. S. Michalski, editors. Machine Learning: A Guide to Current Research. Kluwer Academic Publishers, 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....context rule takes as input a particular state and the sequence of all states in the last search and returns a pattern to be inserted into the database. A search context rule is a deterministic procedure that builds up a pattern given the previously mentioned inputs. In concept induction schemes [9, 11, 13, 16] the goal is to find a concept description to correctly classify a set of positive and negative examples. In general, the smaller description that does the job, the better. Sometimes the concept description needs to be made more specific to make a further distinction. At other times it can be ....

T. M. Mitchell, J. G. Carbonell, and R. S. Michalski, editors. Machine Learning: A Guide to Current Research. Kluwer Academic Publishers, 1986.

....search and returns a pattern to be inserted into the database. A search context rule is just a deterministic procedure that builds up a pattern given the previously mentioned inputs. Examples of search context rules can be found in Section 0.3.3. In concept induction schemes (Michalski, 1983; Mitchell et al. 1986a; Niblett and Shapiro, 1981; Quinlan, 1986) the goal is to find a concept description to correctly classify a set of positive and negative examples. In general, the smaller description that does the job, the better. Sometimes the concept description needs to be made more specific to 2.2. Adding ....

....one. Successive weights in the macro sequence will gradually approach a favorable reinforcement; thus, the system is then motivated to move in this direction. Reverse engineering extends a macro sequence by adding a pattern. This extension is similar to Explanation Based Generalization (EBG) (Mitchell et al. 1986b) or goal regression. The idea is to take the most important pattern in one state, s 1 , and back it up to get its preconditions in the preceding state, s 2 . These preconditions then form a new pattern p 2 . If pattern p 2 is the most useful pattern in state s 2 , it will be backed up as well, ....

T. M. Mitchell, J. G. Carbonell, and R. S. Michalski, editors. Machine Learning: A Guide to Current Research. Kluwer Academic Publishers, 1986.

....Learning, Concept Learning, Theory Revision, First order Logic, Inductive Logic Programming. 1. Introduction Automatic concept formation has long been a major research topic in machine learning [1, 2, 3] Two representative methods of concept formation that are frequently found in the literature [4, 5] are empirical learning and explanation based learning (henceforth referred to as EBL) Empirical learning is the process of acquiring generalized knowledge from examples with few or no background knowledge [6, 7] Usually, a large number of examples are needed to accomplish the task of concept ....

J. G. Mitchell, T. M.and Carbonell and R. S. Michalski, Machine Learning -- A guide to current research, Kluwer Academic (1986).

....and mutation operators as in genetic algorithms 3. goal and subgoal regression as in explanation based generalization. 4. node ordered induced subgraphs The proper mix of these methods is an important issue currently being explored. Generalization and Specialization In concept induction schemes [28,31,33,36] the goal is to find a concept description to correctly classify a set of positive and negative examples. In general, the smaller description that does the job, the better. Sometimes the concept description needs to be made more specific to make a further distinction whereas at other times it can ....

T. M. Mitchell, J. G. Carbonell, and R. S. Michalski, editors. Machine Learning: A Guide to Current Research. Kluwer Academic Publishers, 1986.

....P t = P t 1 , whereas in the cumulative outcome case these are P t = P t 1 c t 1 . Third, construct an update rule that uses the mismatch in the recursive equations to drive weight changes towards a better match. These three steps are very similar to those taken in formulating a dynamic programming problem (e.g. Denardo, 1982). 6. Related Research Although temporal difference methods have never previously been identified or studied on their own, we can view some previous machine learning research as having used them. In this section we briefly review some of this previous work in light of the ideas developed here. ....

In T. M. Mitchell, J. G. Carbonell, & R. S. Michalski (Eds.), Machine learning: A guide to current research. Boston: Kluwer Academic. Denardo, E. V. (1982). Dynamic programming: Models and applications. Englewood Cliffs, NJ: Prentice-Hall.

....as in genetic algorithms 3. goal and subgoal regression as in explanationbased generalization. 4. node ordered induced subgraphs The proper mix of these methods is an important issue currently being explored. Generalization and Specialization In concept induction schemes [ Michalski, 1983; Mitchell et al. 1986; Niblett and Shapiro, 1981; Quinlan, 1986 ] the goal is to find a concept description to correctly classify a set of positive and negative examples. In general, the smaller description that does the job, the better. Sometimes the concept description needs to be made more specific to make a ....

T. M. Mitchell, J. G. Carbonell, and R. S. Michalski, editors. Machine Learning: A Guide to Current Research. Kluwer Academic Publishers, 1986.

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T. M. Mitchell, J. G. Carbonell, and R. S. Michalski. Machine Learning: A Guide to Current Research. Kluwer Academic Publishers, 1986.

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Mitchell, J. G. Carbonell, & R. S. Michalski, (Eds.), Machine learning: A guide to current research. Hingham, MA: Kluwer. Kedar-Cabelli, S. T., & McCarty, L. T. (1987). Explanation-based generalization as resolution theorem proving. Proceedings of the Fourth International Workshop on Machine Learning (pp. 383-389).

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T.M. Mitchell, J.G. Carbonell and R.S. Michalski, "Machine learning: A guide to current research", Kluwer Academic, New York, 1986.

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Mitchell, T. M. , Carbonell, J. G. and Michalski, R. S. (eds) (1986). Machine Learning : A Guide to Current Research, Kluwer, Boston.

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