Quinlan, R. and Cameron-Jones, R. (1993). Foil: A midterm report. In European Conference on Machine Learning (ECML).  Home/Search   Document Details and Download   Summary   Related Articles   Check

....set. More recently, a lazy propositionalization method has been proposed for the system PROPAL, which selectively propositionalizes the FOL training set by interleaving attribute value reformulation and algebraic resolution [3] The main representative of the second class of methods is FOIL 6. 0 [26], which automatically produces comparative literals of type V i k,V i # k, V i V j ,V i # V j , where V j are numerical variables already present in other non comparative literals and k is a numerical threshold. The semantics of the builtin relational predicates, as well as the heuristics for ....

J.R. Quinlan and R.M. Cameron-Jones, FOIL: A midterm report, in: Machine Learning: ECML-93, Lecture Notes in Artificial Intelligence, (Vol. 667), P.B. Brazdil, ed., Springer-Verlag, Berlin, 1993, pp. 3--20.

.... structure) and on the arti cial problem of Eastbound Trains proposed by Ryszard Michalski (prediction of trains directions based on their properties) For the two ILP domains, predictive accuracy is estimated by 10 and 5 fold cross validation, respectively, and results are compared to FOIL [20], Fors [9] and Progol. For the Eastbound Trains, the data is split into one training and test set partition, and the results are averaged over 8 iterations of the experiment. Predictive accuracy RIB is higher than or on par with the one of L. Pe na Castillo, unpublished, 2002 4 M. A. Krogel, ....

J.R. Quinlan and R. M. Cameron-Jones. FOIL: A Midterm Report. In P. Brazdil, editor, Proc. of the 6th European Conference on Machine Learning, 667: 3-20, 1993.

....relation instances (RDF triples) that is needed for the Semantic Web and the intelligent tools it promises. This paper explores the problem of learning information extraction rules that accurately derive ground facts characterising the content of natural language texts. We use the FOIL ILP learner[13], and therefore the problem becomes one of constructing the appropriate representation of the text, and of the background knowledge that is available. Naturally, this must be done automatically. The relations that are learned are those defined in a pre existing ontology of the domain. These ....

Quinlan, R.J. and Cameron-Jones, R.M. FOIL: A midterm report. Proc. European Conference on Machine Learning 1993.

....In this case, applying the CWA would yield a set S that contains all facts of the form v 1 (c 1 ; c 2 ; c 3 ; c 4 ) such that each c i appears in either S or B, but v 1 (c 1 ; c 2 ; c 3 ; c 4 ) 62 S . One off the shelf ILP system that supports use of the CWA is FOIL [Quinlan, 1990; Quinlan and Cameron Jones, 1993] FOIL allows one to specify that a set of positive examples is complete, and will automatically apply the CWA to any complete set of positive examples to obtain negative examples. FOIL also has a number of other features that are desirable for this problem: it is specialized to learn Datalog ....

J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In Pavel B. Brazdil, editor, Machine Learning: ECML-93, Vienna, Austria, 1993. Springer-Verlag. Lecture notes in Computer Science # 667.

....this area is to identify and analyze these special properties. For example, in many examples in which FOIL has learned recursive logic programs, it has made use of complete example sets datasets containing all examples of or below a certain size, rather than sets of randomly selected examples [Quinlan and Cameron Jones, 1993]. It is possible that complete datasets allow a more expressive class of programs to be learned than random datasets. Finally, and most importantly, this paper has established the boundaries of learnability for determinate recursive programs in the pac learnability model. In most plausible ....

J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In Pavel B. Brazdil, editor, Machine Learning: ECML-93, Vienna, Austria, 1993. Springer-Verlag. Lecture notes in Computer Science # 667.

.... 1996 ] with respect to their usual semantic interpretation of primitive concepts and roles, we show that reasoning in DL can be simulated by reasoning in horn logic with simple numeric constraints as formalized in [ Sebag and Rouveirol, 1996 ] and as present in most ILP systems, e.g. Foil [ Quinlan and Cameron Jones, 1993 ] or Progol [ Muggleton, 1995 ] A simple invertible function encodes normalized concept descriptions into horn clauses using new predicates with an external semantics (as Borgida has show they are not expressible in horn logic) to represent the DL terms. The aim of this encoding, of course, is ....

.... i.e. should be # subsumed by #(C, X) for any concept description C, e.g. by any subclause containing the relation Formally this corresponds to reasoning and learning with simple numeric constraints as present in Constraint Logic Programs (CLP) In in most ILP systems, e.g. Foil [ Quinlan and Cameron Jones, 1993] or Progol [ Muggleton, 1995 ] this is done with the help of the computed built in predicates and or #. For Foil or Progol a more suitable encoding is rrR (X, n atleast , matmost , Y ) as they can learn cmin and cmax in literals like cmin n atleast and matmost cmax . Sebag and ....

Quinlan, R. and R. M. Cameron-Jones: 1993, `FOIL: A Midterm Report'. In: P. Brazdil (ed.): Proceedings of the Sixth European Conference on Machine Leaning (ECML-93). Berlin, Heidelberg, pp. 3--20, Springer Verlag.

....can be obtained from their discrete domains in a straightforward manner without prior partitioning. Usually, for symbolic data an explicit ordering scheme is given, like rule inference, which can be represented as data driven decision tree with labeled leaves [1, 2] Classic programs like FOIL [3] and GOLEM [4] both provide an induction of Horn clauses from data, but the domain of ILP has been extended to dynamic hypothesis generation [5] learning recursive logic [6] and program synthesis [7, 8] Soft representations are especially suitable for real value data, because the natural order ....

J.R. Quinlan and R.M. Cameron-Jones. FOIL: A Midterm Report. In P. Brazdil (ed.), Proceedings of the 6th European Conference on Machine Learning, Vol. 667, pp. 3--20, Springer, 1993.

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Quinlan, J.R., and Cameron-Jones, R.M., FOIL: a midterm report, in: Proceedings European Conference on Machine Learning, Vienna (Springer-Verlag, Berlin, 1993) 3-20.

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Quinlan, R. and Cameron-Jones, R. (1993). Foil: A midterm report. In European Conference on Machine Learning (ECML).

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Quinlan, J.R., and Cameron-Jones, R.M., 1993. FOIL: a midterm report. In: P. Brazdil (Editor), Proc. European Conference on Machine Learning, Springer Verlag, pp. 3-20.

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J.R. Quinlan and R. M. Cameron-Jones. FOIL: A Midterm Report. In P. Brazdil, editor, Proc. of the 6th European Conference on Machine Learning, 667: 3-20, 1993.

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J.R. Quinlan and R. M. Cameron-Jones. FOIL: A Midterm Report. In P. Brazdil, editor, Proceedings of the 6th European Conference on Machine Learning, volume 667, pages 3-20. Springer-Verlag, 1993.

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Quinlan, J.R. (1993): FOIL: A Midterm Report. Proceedings of ECML-93, Springer-Verlag.

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J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In Proc. 1993.

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J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In European Conf. Machine Learning, 1993.

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J. R. Quinlan and R. M. Cameron-Jones, "FOIL: A midterm report," in Machine Learning: ECML-93, 1993, pp. 3--20, http: //www.cse.unsw.edu.au/ # quinlan/q+cj.ecml93.ps. 87

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J.R. Quinlan and R. M. Cameron-Jones. FOIL: A Midterm Report. In P. Brazdil, editor, Proc. of the 6th European Conference on Machine Learning, 667: 3-20, 1993.

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Quinlan, J.R., Cameron-Jones, R.M.: FOIL: A midterm report, in Proceedings of the 6th European Conference on Machine Learning, LNAI 667, pp. 3-20, Springer-Verlag, 1993

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J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In Proc. 1993.

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Quinlan, R.: FOIL: A midterm report. In: Proceedings of the 6th European Conference on Machine Learning, volume 667 of Lecture Notes in Artificial Intelligence, Springer-Verlag (1993)

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J. R. Quinlan and R. M. Cameron-Jones. FOIL: A midterm report. In P. B. Brazdil, editor, Machine Learning: ECML-93, Vienna, Austria, 1993. Springer-Verlag. Lecture notes in Computer Science # 667.

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J.R. Quinlan. FOIL: A midterm report. In P. Brazdil, editor, Proceedings of the 6th European Conference on Machine Learning, Lecture Notes in Arti cial Intelligence. Springer-Verlag, 1993.

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Quinlan, J. R., Cameron-Jones, R. M.: FOIL: A Midterm Report. Proc. of the European Conference on Machine Learning. (1993) 3--20

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J.R. Quinlan and R. M. Cameron-Jones. FOIL: A Midterm Report. In P. Brazdil, editor, Proceedings of the 6th European Conference on Machine Learning, volume 667, pages 3-20. Springer-Verlag, 1993.

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J. R. Quinlan. FOIL: a midterm report. In European Conference on Machine Learning. Springer-Verlag, Berlin, 1993.

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