A robust machine learning approach for signal separation and classification

Simone Filice, Danilo Croce, Roberto Basili

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper a data-driven approach for signal separation over the digital domain is discussed. The proposed approach solves the problem as a classification task and it is widely experimented over electromagnetic signals in open scenarios. Results show that high levels of accuracy are reachable through a relatively easy learning method over simulated data.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages749-757
Number of pages9
Volume7887 LNCS
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013 - Funchal, Madeira
Duration: 5 Jun 20137 Jun 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7887 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013
CityFunchal, Madeira
Period5/6/137/6/13

Fingerprint

Learning systems
Machine Learning
Data-driven
Scenarios
Learning

Keywords

  • Machine Learning
  • Pattern recognition
  • Signal Processing
  • Support Vector Machines

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Filice, S., Croce, D., & Basili, R. (2013). A robust machine learning approach for signal separation and classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7887 LNCS, pp. 749-757). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7887 LNCS). https://doi.org/10.1007/978-3-642-38628-2_89

A robust machine learning approach for signal separation and classification. / Filice, Simone; Croce, Danilo; Basili, Roberto.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7887 LNCS 2013. p. 749-757 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7887 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Filice, S, Croce, D & Basili, R 2013, A robust machine learning approach for signal separation and classification. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7887 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7887 LNCS, pp. 749-757, 6th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2013, Funchal, Madeira, 5/6/13. https://doi.org/10.1007/978-3-642-38628-2_89
Filice S, Croce D, Basili R. A robust machine learning approach for signal separation and classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7887 LNCS. 2013. p. 749-757. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-38628-2_89
Filice, Simone ; Croce, Danilo ; Basili, Roberto. / A robust machine learning approach for signal separation and classification. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7887 LNCS 2013. pp. 749-757 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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