Dataset Cataloging Metadata for Machine Learning Applications Research

Sally Jo Cunningham
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:139-146, 1997.

Abstract

As the field of machine learning (ML) matures, two types of data archives are developing: collections of benchmark data sets used to test the performance of new algorithms, and data stores to which machine learning/data mining algorithms are applied to create scientific or commercial applications. At present, the catalogs of these archives are ad hoc and not tailored to machine learning analysis. This paper considers the cataloging metadata required to support these two types of repositories, and discusses the organizational support necessary for archive catalog maintenance.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR1-cunningham97a, title = {Dataset Cataloging Metadata for Machine Learning Applications Research}, author = {Cunningham, Sally Jo}, booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics}, pages = {139--146}, year = {1997}, editor = {Madigan, David and Smyth, Padhraic}, volume = {R1}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r1/cunningham97a/cunningham97a.pdf}, url = {https://proceedings.mlr.press/r1/cunningham97a.html}, abstract = {As the field of machine learning (ML) matures, two types of data archives are developing: collections of benchmark data sets used to test the performance of new algorithms, and data stores to which machine learning/data mining algorithms are applied to create scientific or commercial applications. At present, the catalogs of these archives are ad hoc and not tailored to machine learning analysis. This paper considers the cataloging metadata required to support these two types of repositories, and discusses the organizational support necessary for archive catalog maintenance.}, note = {Reissued by PMLR on 30 March 2021.} }
Endnote
%0 Conference Paper %T Dataset Cataloging Metadata for Machine Learning Applications Research %A Sally Jo Cunningham %B Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1997 %E David Madigan %E Padhraic Smyth %F pmlr-vR1-cunningham97a %I PMLR %P 139--146 %U https://proceedings.mlr.press/r1/cunningham97a.html %V R1 %X As the field of machine learning (ML) matures, two types of data archives are developing: collections of benchmark data sets used to test the performance of new algorithms, and data stores to which machine learning/data mining algorithms are applied to create scientific or commercial applications. At present, the catalogs of these archives are ad hoc and not tailored to machine learning analysis. This paper considers the cataloging metadata required to support these two types of repositories, and discusses the organizational support necessary for archive catalog maintenance. %Z Reissued by PMLR on 30 March 2021.
APA
Cunningham, S.J.. (1997). Dataset Cataloging Metadata for Machine Learning Applications Research. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R1:139-146 Available from https://proceedings.mlr.press/r1/cunningham97a.html. Reissued by PMLR on 30 March 2021.

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