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Unsupervised Data Selection for Supervised Learning

Authors :
Valvano, Gabriele
Leo, Andrea
Della Latta, Daniele
Martini, Nicola
Santini, Gianmarco
Chiappino, Dante
Ricciardi, Emiliano
Publication Year :
2018

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.<br />Comment: 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

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.1810.12142
Document Type :
Working Paper