Machine Learning: The Necessity of Order (is order in order ?)

Antoine Cornuéjols 1, 2
2 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France, X - École polytechnique, CNRS - Centre National de la Recherche Scientifique : UMR7161
Abstract : In myriad of human-tailored activities, whether in the classroom or listening to a story, human learners receive selected pieces of information, presented in a chosen order and pace. This is what it takes to facilitate learning. Yet, when machine learners exhibited sequencing effects, showing that some data sampling, ordering and tempo are better than others, it almost came as a surprise. Seemingly simple questions had suddenly to be thought anew : what are good training data? How to select them? How to present them? Why is it that there are sequencing effects? How to measure them? Should we try to avoid them or take advantage of them? This chapter is intended to present ideas and directions of research that are currently studied in the machine learning field to answer these questions and others. As any other science, machine learning strives to develop models that stress fundamental aspects of the phenomenon under study. The basic concepts and models developed in machine learning are presented here, as well as some of the findings that may have significance and counterparts in related disciplines interested in learning and education.
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Chapitre d'ouvrage
F. Ritter, J. Nerb, E. Lehtinen & T. O'Shea (Eds.). In order to learn: How the sequences of topics affect learning, Oxford University Press, 2006
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Contributeur : Antoine Cornuéjols <>
Soumis le : jeudi 11 janvier 2007 - 07:00:01
Dernière modification le : jeudi 10 mai 2018 - 02:06:24
Document(s) archivé(s) le : jeudi 20 septembre 2012 - 15:50:09

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Antoine Cornuéjols. Machine Learning: The Necessity of Order (is order in order ?). F. Ritter, J. Nerb, E. Lehtinen & T. O'Shea (Eds.). In order to learn: How the sequences of topics affect learning, Oxford University Press, 2006. 〈inria-00119757〉

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