Download Inductive Logic Programming: 16th International Conference, by Vladimir Lifschitz (auth.), Stephen Muggleton, Ramon Otero, PDF
By Vladimir Lifschitz (auth.), Stephen Muggleton, Ramon Otero, Alireza Tamaddoni-Nezhad (eds.)
The inherent risks of swap are usually summed up within the misquoted chinese language curse “May you reside in attention-grabbing times.” The submission approach for the sixteenth foreign convention of Inductive good judgment Programming (ILP 2006) used to be an intensive (hopefully attention-grabbing yet no longer cursed) departure from prior years. Submissions have been asked in levels. The ?rst section concerned submission of brief papers (three pages) that have been then awarded on the convention and incorporated in a brief papers court cases. within the moment section, reviewers chosen papersforlongpapersubmission(15pagesmaximum).Thesewerethenassessed via a similar reviewers, who then made up our minds which papers to incorporate within the magazine designated factor and court cases. within the ?rst part there have been a list seventy seven papers, comparedto the usual20 orso lengthy papersofpreviousyears.Eachpaper used to be- seen via 3 reviewers. Out of those, seventy one participants have been invited to put up lengthy papers. Out of the lengthy paper submissions, 7 have been chosen for the - chine studying magazine unique factor and 27 have been permitted for the complaints. furthermore, papers have been nominated through software Committee referees for the purposes prize and for the idea prize. The papers symbolize the div- sity and energy in current ILP learn together with ILP idea, implementations, seek and section transition, allotted and large-scale studying, probabilistic ILP, organic purposes, ordinary language studying and making plans and motion learning.
Read or Download Inductive Logic Programming: 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers PDF
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Extra resources for Inductive Logic Programming: 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers
Example text
The main conclusion from our experiments is that generalized ordering-search succeeds in learning an ordering that performs approximately equally well (in terms of likelihood) as the expert orderings and considerably better than random orderings. Also, the complexity (size) of the learned CPDs for generalized ordering search is similar to that for the expert orderings and smaller than for random orderings. Acknowledgements TC and DF are supported by the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT Vlaanderen).
Riguzzi to the input interpretations a probability that is as close as possible to the one given. This is done by exploiting theorem 2 of [3]. Since the optimization problem can be expressed as a linear problem, we can use mixed-integer programming (MIP) techniques. If no perfect solution exists, a non zero optimum will be found. However, the optimization problem is NP-hard and thus solvable only for small instances. To overcome this problem, we exploit the possibility of setting a time limit offered by many MIP packages.
Springer, Heidelberg (2001) 5. : Towards combining inductive logic programming and Bayesian networks. , Sebag, M. ) ILP 2001. LNCS (LNAI), vol. 2157, pp. 118– 131. Springer, Heidelberg (2001) 6. : Dependency networks for relational data. In: Proceedings of the 4th IEEE International Conference on Data Mining (2004) 7. : Generalized ordering-search for learning directed probabilistic logical models. Machine Learning (special issue ILP’06), 2007 Conditionally accepted 8. : Ordering-based search: A simple and effective algorithm for learning Bayesian networks.