Unsupervised Data Selection for Supervised Learning
Abstract
Recent research put a big effort in the development of deep learning architectures and optimizers obtaining impressive results in areas ranging from vision to language processing. However little attention has been addressed to the need of a methodological process of data collection. In this work we hypothesize that high quality data for supervised learning can be selected in an unsupervised manner and that by doing so one can obtain models capable to generalize better than in the case of random training set construction. However, preliminary results are not robust and further studies on the subject should be carried out.
Cite
@article{arxiv.1810.12142,
title = {Unsupervised Data Selection for Supervised Learning},
author = {Gabriele Valvano and Andrea Leo and Daniele Della Latta and Nicola Martini and Gianmarco Santini and Dante Chiappino and Emiliano Ricciardi},
journal= {arXiv preprint arXiv:1810.12142},
year = {2018}
}
Comments
Technical Report --- 8 pages, 3 figures New tests demonstrated that the system, as is, is not able to create reproducible results. Further study on the topic should be done