Metric Learning for Multi-label Classification View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2021-04-10

AUTHORS

Marco Brighi , Annalisa Franco , Dario Maio

ABSTRACT

This paper proposes an approach for multi-label classification based on metric learning. The approach has been designed to deal with general classification problems, without any assumption on the specific kind of data used (images, text, etc.) or semantic meaning assigned to labels (tags, categories, etc.). It is based on clustering and metric learning algorithm aimed at constructing a space capable of facilitating and improving the task of classifiers. The experimental results obtained on public benchmarks of different nature confirm the effectiveness of the proposal. More... »

PAGES

24-33

Book

TITLE

Structural, Syntactic, and Statistical Pattern Recognition

ISBN

978-3-030-73972-0
978-3-030-73973-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-73973-7_3

DOI

http://dx.doi.org/10.1007/978-3-030-73973-7_3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1137035315


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